By Mike Levin

Replacing Homegrown AI Server Tool-Calling with MCP

Having gutted Pipulate of its homegrown tool-call feature that worked with any LLM model, even back to gemma2:2b which is the 2 billion quantized model on Ollama, I am now left with an LLM embedded into my FastHTML web app which can only chat and instruct you through the Notebook-based linear workflows using real-time injected domain expertise – but not actually reach out and perform the steps for you. That’s the goal with the mcp (Model Context Controller) here – an official new protocol to do just that.

The Journey from Tightly Coupled to Plugin Architecture

Admittedly, even with my hacked together JSON executer, it could only CREATE records in the Todo CRUD (Create, Read, Update, Delete) database app that was built into core – tightly coupled with the core-app, as they say. But since I yanked it out into a plugn when implemented a auto-discovery plugin system (for both the CRUD db plugin apps and the Notebook Workflows), it lost even the specification ability I left for myself as a sort of to-do item I could not forget because of how big of a chunk of code it was.

Real-Time LLM Training Through Navigation

Well, the good news is that it will shed this homegrown tool-call aspect that even the LLMs themselves who were real-time trained to use it called a brittle hack, haha! I had to perform a hot-prompt injection carrying out real-time fine-tuning every time Tasks was chosen from the dropdown menu. That’s right: site navigation controls prompts, priming the pump for LLM-expertise in whatever area of the program you just navigated to! LLMs aren’t just for chat-boxes anymore. You’re welcome.

Racing Against Client Work Deadlines

But here we are 6:45 AM on a Monday, and in extremely short order the client work takes precedent. So this is going to be a rapid documentation of next-step process, and unlikely to go into implementation at all. I will drop a few reminders to myself directly into the code where the MCP wedge will go. So I shall set myself a little race as the sun rises to get to a good task-switching point, changing focus satisfied that I left off at a better place and with a deeper understanding of the state of MCP in the world today.

Documentation Challenges and Vendor Bias

When last I tried to do the MCP QuickStart guide off of the Anthropic site GitHub list, I made the disheartening discovery that the documentation was out of sync with the libraries, and all the examples out there were heavily vendor-biased. This is understandably a technology for vendors, mostly AI Assisted editor vendors like Cursor AI, Windsurf, Cline and most important of all – Anthropic’s standaline Claude Coder! Brilliant!

The Brilliance of MCP Branding

Oh, a word on Anthropic and their brilliant branding. MCP?!?! I mean come on, in one fell swoop you step into the web-traffic path on both Microsoft Certified Professionals and the metacarpophalangeal joint, which is the same carpal as CTS (carpal tunnel syndrome), another issue very much affecting programmers. And the metacarpal bones are the very ones that enable humans to have great dexterity in their hands to use tools. I mean, talk about meta! Oh, and how about being similar to MVC (model view-controller) made popular by Ruby on Rails? Or MVP which everyone uses for minimum viable product these days? The use of MCP for model context protocol is feature-branding brilliance!

The Need for Protocol Standardization

I must also add that as a new protocol, this must be as branding success!. The unspoken joke here is that there is a schism between the API function tool-calling specifications from OpenAI (who pioneered it), Google and Meta (Llama) who all quickly jumped into the bandwagon to support AIs executing functions (albeit indirectly, for security). The rub is that the LLMs themselves need to understand each schism of the function tool-calling protocols to construct well-formed and valid replies.

Historical Parallels with XML and EDI

Few people reading this will remember when XML burst onto the scene for similar reasons (replacing EDI - electronic data interchange). EDI came even before the Internet. Yeah, computers still had to talk to each other back then. But all this democratizing stuff wasn’t here yet – not even JSON. So, XML (subset of SGML just like HTML but made for data) was chosen for the purpose. But XML data had to be both well-structured and valid – well-formed according to the XML protocol in general, and valid according to the application’s particular schema, or else the XML parser would choke (not use the data and hopefully return an error).

The Challenge of Training LLMs on New Protocols

Similarly today, LLMs need to return JSON-structured responses that are both well-formed and valid according to whatever response-processing tool they’re calling. That was true with the older OpenAI tool-call syntax, and its true now with the model context protocol. You don’t get away from training your AI to understand the protocol. The only question is whether you thrust the burden of understanding a dozen fragmented subtly different protocols that really undermines everything and everyone. Or whether you settle on a single standard and drill that fact into the models themselves. See? MCP has to be a marketing success! Everyone has to adopt standard protocols for them to become standards and today everyone especially has to include LLMs themselves.

The Cost of Protocol Implementation

But it is a multi-billion dollar proposition to drill-into the models all the new rules! Retroactively editing history so that the new stuff overrules the old is not easy. Grover Cleveland has the unique distinction of being the only president in history to serve two non-consecutive terms, and good luck convincing them otherwise. Either real-time RAG systems need to recognize and intercept time-sensitive or particular subject-matter queries and augment the default response the core static model would produce, or the core static models themselves need to be retrained or fine-tuned. Expensive! And doing this for protocols you’re just now inventing for the sake of competition is shooting yourself and everyone else in the foot.

The Case for Universal Protocol Adoption

Why have multiple tool-call protocols when you could drill-in only one universal-adapter version (as Anthropic calls it) and have all the models just understand MCP. The writing’s on the wall that this is the one true way. But for the models to adapt and get how to use the mcp protocol trained into their core static selves, they’ve got to start sooner rather than later. In this AI horserace, these details matter. MCP support is getting into all the AI-assisted coding environments as plugins and marketplaces. Thus, the social media frenzy surrounding this topic. Your model must support them, or else your model won’t be chosen from within the editors and it won’t be your cloud cash register going ka-ching on every code completion.

Early Adoption Challenges with Claude Desktop

Oh yeah, Claude Desktop! It’s funny how certain topics jog your memory. When I first tried this out, I got tremendously frustrated and thought it only worked there. The cash-register must go ka-ching on Claude first, naturally. He who invents the protocol gets the first-mover advantage. And so all we early adopters jump on that bandwagon, using either the Claude Desktop or Cursor AI, which it’s Claude built into anyway, so all their MCP fine-tuning looks really good. MCP works! It works on Claude Desktop with the Claude model… and the documentation gets out-of-sync for other use cases, haha!

Embracing Change Despite Resistance

That’s where I left off, heckling the MCP state of affairs like Statler and Waldorf. Those are the 2 old-guy Muppets hating on everything in the memes, for you youngin’s here. I’m a curmudgeonly old Luddite who loves craft and my tools to last forever, but knows they can’t and am determined to reinvent. Thus articles like this, because it goes against the grain of my nature. But writing about it and actually eventually adapting to the problem doesn’t. So, we re-examine the state of MCP… finally!

Research Objectives for MCP Implementation

Hey you researching AI-madman: do your research! Your objective is to derive my objective from this full article, which I’m sure goes something like scouring the Internet for the latest on the MCP protocol. How was it able to pop up in so many places so fast when I found documentation inaccuracy problems. Surely they found the same problems, but because there is groundswell support occurring and money to be made because of the plugin ecosystem environments, surely they’ve adapted better than I’ve been able to. Also, they’re in the sweet spot of publishing MCP… what, exactly?

Understanding MCP Client-Server Architecture

If your AI assisted code editor like Cursor AI, Cline and Claude Desktop are the clients, which is my understanding, and they consume resources either on your machine that you make available as an MCP server like database access or an automated web browser, then what are the plugins in the marketplaces? Extensions to the client to let them know how to make MCP requests of servers which are things elsewhere? And why are they popping up all over the place apparently so easily?

Pipulate as an MCP Client

In my use-case I’m making the equivalent of an MCP client. That’s my web framework that has a local LLM embedded through Ollama. It is definitely the client or consumer of services. And to know how to ask for those services, either locally or over the http protocol, it needs to know another protocol called MCP. That much is apparent. So, Pipulate (the name of my framework) is destined to become an MCP client. It’s going to have API function tool-call abilities almost like OpenAI’s original API feature, but through slightly different terminology and procedure in order to be that universal adapter.

Exploring Non-HTTP MCP Implementation

Making servers seems straight forward enough, especially over http where you can just use Uvicorn or the http server built natively into Python. But if you don’t want the performance hit and complexity overhead of all these multiple servers running all over the place (echos of microservices), there must be some way to simplify it. MCP without HTTP? This is another part of what to research.

Final Implementation Goals

So, I guess it comes down to documenting MCP well according to the ACTUAL state of affairs that you can gather from Anthropic, GitHub, PyPI, Reddit and wherever else such up-to-the-second things are discussed and provide me a complete breakdown of how I can today start putting the hooks into a locally running FastHTML (not FastAPI) web framework that has both an plugin framework for both Rails-like CRUD database apps that the LLM will have to know how to do CRUD operations for, and Jupyter Notebook-inspired linear Workflow plugin apps which may need to do almost anything, touching all sorts of outside MCP servers for which the protocol’s apparent discovery mechanism (as it appears such things must be necessary) provides.

My goal is to understand the MCP state of affairs well enough to leave detailed comments for MCP-hooks throughout my code.

Deep (Research|research|Search) ChatGPT Qualifying Questions

I start with ChatGPT because it asks me qualifying questions, which I will include in this article super-prompt and use with all the other models as well so they have the same information.

ChatGPT 4.5 Deep research: Thanks for the deep and thoughtful write-up. To provide the most useful and up-to-date breakdown on MCP (Model Context Protocol), could you please confirm a few things so I can tailor the research exactly to your needs:

1. What exact deliverable do you want? A detailed technical guide to implementing MCP in Pipulate? A protocol summary? A tool and ecosystem map? Or all of the above?
2. Are you looking for pure HTTP-based MCP server-client setups, or are you also interested in zero-HTTP setups (e.g., direct in-process function invocation or something like Unix sockets)?
3. Do you want a comparison of MCP with other competing tool-call approaches (like OpenAI function calling, Google’s system, etc.)?
4. Should the research focus only on Claude-based implementations, or also cover how MCP is being adapted elsewhere (Cursor, Cline, etc.)?

Once I know your ideal outcome from this task, I can dive into it and give you a fully documented picture of MCP’s real-world implementation status.

Me:

1. All of the above.
2. Both. I definitely prefer zero-HTTP as my app was born as a local tightly coupled app, and I view microservices as an application of Conway’s Law.
3. No, the old tool-call protocols are water over the bridge. Let’s assume MCP is the future.
4. Oh, definitely not only Claude based. My plan is to make an local Ollama-powered MCP client! That’s what Pipulate is. I effectively have to do what the proprietary MCP clients are doing today. That’s what I seek. And there are pitfalls here, I believe.

Summarizing The Article So Far

Okay, here are the requested items based on the provided article:

Summary (First-Person Author Perspective):

[Moved to top]

Title/Headline Ideas:

• Integrating MCP: Giving My Local LLM App (Pipulate) the Power to Act
• Beyond Instruction: The Quest to Implement MCP Tool-Calling in Pipulate
• From Homegrown Hacks to a Standard: Adopting Model Context Protocol
• MCP Deep Dive: Researching the Path to a Smarter, Action-Oriented Pipulate
• Navigating the MCP Landscape: A Developer’s Plan for Local LLM Tool-Calling
• Pipulate Needs Hands: Embracing MCP for Real-World LLM Integration
• The State of MCP: Can This Protocol Solve LLM Tool-Calling Fragmentation?
• Why Model Context Protocol is the Future (and How I Plan to Use It)
• Decoding MCP: A Pragmatic Approach to Adding Tool Use to a Local LLM

AI Opinion of the Article:

The article provides a highly detailed and candid look into the practical challenges and thought processes of a developer working at the intersection of web application development and rapidly evolving Large Language Model capabilities. The author effectively communicates the limitations of their previous approach, the strategic reasoning behind choosing MCP as a potential standard despite its perceived immaturity and documentation issues, and the specific technical goals for their application, Pipulate. The blend of technical discussion (mentioning specific frameworks like FastHTML, Ollama, and concepts like CRUD, plugins, RAG), strategic commentary on protocol standardization and branding, and personal reflection on the development workflow makes for an engaging and informative piece. It clearly outlines a real-world problem and a specific research objective driven by both technical necessity and a forward-looking view of AI integration. The inclusion of the clarifying questions asked by ChatGPT and the author’s answers further sharpens the focus of the intended research.

Grok 3 Could Not Complete Response… But Then Did!

When I put the first 3 research engines in their 3-way horserace, the big surprise was that even though I started Grok3 AFTER ChatGPT and Gemini, it still completed first… so I thought. It couldn’t complete the request, so I hit retry and it succeeded, but not before Gemini finished. Still, I put it first for this distinguishing behavior. Anthropomorphizing aside, it’s very enthusiastic!

Key Points

• Research suggests MCP (Model Context Protocol) is an emerging standard for connecting AI models to tools and data, likely to become widely adopted due to its open nature.
• It seems likely that implementing MCP in Pipulate, your local web framework with an embedded LLM, will enable seamless integration with various data sources and tools.
• The evidence leans toward HTTP-based communication being standard for MCP, but zero-HTTP setups are possible for local optimization, though they may require custom implementation.
• There’s growing support for MCP in AI-assisted coding environments, suggesting it’s becoming a key feature for developer tools.

Understanding Your Objective

Your goal is to integrate MCP into Pipulate, a FastHTML web framework with a locally embedded LLM via Ollama, to enable it to act as an MCP client. You want to understand the current state of MCP, document how to implement it, and leave detailed comments for MCP hooks in your code. This includes both HTTP-based and zero-HTTP setups, focusing on connecting to external MCP servers and potentially creating local ones for database and workflow operations.

Implementing MCP in Pipulate

MCP is designed to standardize how AI applications, like Pipulate, interact with external tools and data sources. Here’s how you can start:

• Set Up the MCP Client: Use the Python SDK for MCP, available at GitHub. Install it with pip install mcp, then create a client to connect to MCP servers, fetching tools, resources, and prompts.
• Integrate with Your LLM: When prompting your LLM, include descriptions of available tools from MCP servers. Handle tool calls by parsing the LLM’s response and forwarding requests to the appropriate server, then feeding results back to the LLM.
• Create Local MCP Servers: For local data sources like your CRUD database apps, implement custom MCP servers using the SDK. Define tools (e.g., for database queries) and run them locally, accessible via HTTP by default.
• Consider Zero-HTTP Setups: While MCP typically uses HTTP, for local performance, you can run servers and clients in the same process, potentially using in-memory communication, though this may need custom implementation beyond the standard SDK.

Security and Best Practices

Ensure user consent for tool execution, protect data with access controls, and treat tool descriptions as untrusted unless from trusted servers, as outlined in the MCP Specification.

Survey Note: Comprehensive Breakdown of MCP Implementation in Pipulate

Introduction to Model Context Protocol (MCP)

The Model Context Protocol (MCP), developed by Anthropic and open-sourced as of recent updates, is an emerging standard aimed at standardizing how AI applications provide context to Large Language Models (LLMs). It addresses the challenge of fragmented integrations by offering a universal protocol for connecting AI systems with data sources, tools, and services. As of March 31, 2025, MCP is gaining traction, particularly in AI-assisted coding environments like Cursor AI, Cline, and Claude Desktop, with a growing ecosystem of servers and plugins.

MCP follows a client-server architecture:

• MCP Hosts: Applications like Pipulate that want to access data through MCP.
• MCP Servers: Lightweight programs exposing capabilities such as database access or web browsing.
• MCP Clients: Protocol clients maintaining connections with servers, facilitating communication.

This structure is inspired by the Language Server Protocol (LSP), standardizing AI tool integration across ecosystems. The protocol’s open nature, with specifications dated March 26, 2025, at MCP Specification, suggests it’s poised for widespread adoption, especially given its support in tools like Claude Desktop and Replit.

Current State of MCP: Adoption and Ecosystem

MCP’s rapid adoption is evident from its presence in AI-assisted coding environments. Early adopters like Block and Apollo, along with development tools such as Zed and Codeium, are integrating MCP, enhancing AI agents’ ability to retrieve context for coding tasks. The GitHub repository lists 190 example servers, categorized into Reference Servers (19), Official Integrations (63), and Community Servers (108), showcasing its versatility. These include servers for Google Drive, Slack, GitHub, and niche tools like Ableton Live and Unity3D, indicating a robust ecosystem.

The protocol’s popularity is driven by its plugin marketplaces, where extensions enable clients like Cursor AI to make MCP requests to servers. This groundswell support, despite initial documentation inaccuracies noted in your experience, suggests developers are adapting, with resources like DataCamp Tutorial providing practical guides. The social media frenzy, particularly on platforms like X, underscores MCP’s importance, with discussions highlighting its integration into editors as a must-have for cloud revenue.

Implementing MCP Client in Pipulate

Given Pipulate’s role as a local web framework with an embedded LLM via Ollama, it acts as an MCP client, consuming services from MCP servers. Here’s a detailed implementation guide:

1. Installation and Setup:
   • Install the Python SDK with pip install mcp, as detailed in the Python SDK repository.
   • Create an MCP client instance for each server URL, e.g., MCPClient("http://localhost:8000").
2. Fetching Tools and Resources:
   • Use client.get_tools(), client.get_resources(), and client.get_prompts() to fetch available capabilities.
   • Aggregate these from multiple servers to provide a comprehensive list to your LLM.
3. LLM Integration:
   • When prompting your LLM, include tool descriptions in the prompt, e.g., “You have access to search_files: Searches for files on the local filesystem with parameters query and path.”
   • Handle LLM responses for tool calls by parsing JSON outputs like {"tool_call": {"name": "search_files", "args": {"query": "document", "path": "/docs"}}}.
   • Forward tool calls to the appropriate server using client.call_tool(tool_name, args), then feed results back to the LLM for further processing.
4. Workflow Example:
   • For your Notebook-based linear workflows, when a task requires external data, the LLM can call an MCP server tool (e.g., querying a PostgreSQL server) and integrate the result into the workflow, enhancing real-time domain expertise.

This implementation aligns with your goal of replacing the homegrown tool-call feature, leveraging MCP’s universal adapter approach to avoid the brittleness of previous hacks.

Creating Custom MCP Servers for Local Data Sources

For local data sources like your CRUD database apps and Jupyter-inspired workflows, you can implement MCP servers:

• Server Definition:
  • Inherit from MCPServer and define tools, e.g., add_tool("create_record", {...}, self.create_record_function).
  • Implement tool logic, such as querying a local SQLite database or executing a workflow step.
• Running the Server:
  • Use server.run(port=8000) to start the server, accessible via HTTP by default using FastAPI under the hood.
• Example:
  • For a Todo CRUD app, define a tool create_todo with parameters title and description, implementing the logic to insert into your database.

This approach decouples your core app from specific tool implementations, aligning with your plugin system for auto-discovery.

Zero-HTTP Setups: Exploring Alternatives

While MCP standardizes on HTTP-based communication, your preference for zero-HTTP setups to avoid microservices overhead is valid, especially for local applications. The current SDK, as seen in Python SDK, uses FastAPI, implying HTTP. However, for local optimization:

• Run servers and clients in the same process, using in-memory communication like Python queues or shared memory, though this requires custom implementation beyond the SDK.
• Consider Unix sockets for inter-process communication, faster than HTTP but needing manual integration.
• Given Pipulate’s tight coupling, running local HTTP servers might suffice, with minimal overhead for intra-machine requests.

This is an area for further research, as the specification at MCP Specification doesn’t explicitly support non-HTTP methods, but community discussions on GitHub Discussions may offer insights.

Security and Best Practices

MCP’s power comes with security implications, as noted in the Specification. Implementors must:

• Build robust consent flows, ensuring users authorize tool use.
• Provide clear documentation of security implications.
• Implement access controls, treating tool descriptions as untrusted unless from trusted servers.

For Pipulate, ensure your LLM prompts include user consent checks before executing tools, and log all tool calls for auditability.

Comparison with Other Tool-Call Approaches

While you’ve indicated MCP is the future, it’s worth noting its advantage over fragmented protocols like OpenAI’s function calling or Google’s system. MCP’s universality, as highlighted in Medium Post, reduces the burden on LLMs to understand multiple protocols, fostering a single standard. This aligns with historical parallels like XML replacing EDI, ensuring well-formed and valid JSON responses.

Documentation for MCP Hooks in Code

To leave detailed comments for MCP hooks in Pipulate, consider:

• In your LLM interaction module, comment: # MCP Client: Connect to servers and fetch tools for LLM prompt.
• For tool call handling: # MCP Tool Call: Parse LLM response, forward to server, and handle result.
• For local server integration: # MCP Server: Define tools for local database access, run on port 8000.

This ensures you can pick up implementation later, aligning with your rapid documentation goal at 6:45 AM on a Monday.

Tables: Example MCP Servers and Their Categories

These tables summarize the ecosystem and components, aiding your understanding for code integration.

Conclusion

By implementing MCP in Pipulate, you’ll enable your framework to leverage a standardized protocol, enhancing its capabilities for both CRUD operations and workflow plugins. The current state, with robust adoption and documentation, supports your goal of leaving detailed hooks, ensuring a smooth transition to this universal adapter approach.

Key Citations

• Model Context Protocol Introduction
• Introducing the Model Context Protocol Anthropic
• Getting Started Model Context Protocol Medium
• Model Context Protocol GitHub
• Model Context Protocol vs APIs Blog
• Model Context Protocol NSHipster
• Model Context Protocol Explained Sean Goedecke
• Model Context Protocol Specification 2024-11-05
• Model Context Protocol Specification 2025-03-26
• Model Context Protocol Blog Portkey
• Model Context Protocol Servers GitHub
• Model Context Protocol Python SDK GitHub
• MCP Guide with Demo Project DataCamp

Google Gemini’s Deep Research Next, Because It’s FAST!

I have to say, even though Gemini is a pleasure to watch research and looks at sources 100-at-a-time versus the other engine’s 1-at-a-time, I’m still full of trepidation and formatting this! First to Google Docs, then to Markdown, then backslash search/replace hell. I tried the normal Copy Contents but there was no markdown and it was 5 mega-long lines of run-on text. So, Google Docs and NeoVim RegEx Kung Fu, it is!

I’ll just put these here:

:'<,'>s/|/\&#124;/gcc
:'<,'>s/\\\([\-+\[\]_#\.]\)/\1/g

The Model Context Protocol: A Standard for Connecting AI with External Systems

The increasing sophistication of large language models (LLMs) has highlighted a critical challenge: their inherent isolation from real-time data and external tools. While these models exhibit impressive capabilities in reasoning and language generation, their utility in practical applications is often limited by their inability to access and interact with the dynamic information and systems that underpin many real-world tasks. Recognizing this limitation, Anthropic introduced the Model Context Protocol (MCP) on November 25, 2024, with the express aim of establishing a universal and open standard for seamlessly connecting AI assistants to the vast landscape of data repositories, business applications, and development environments.1 Before the advent of MCP, integrating AI with diverse data sources necessitated the creation of bespoke connectors for each specific source. This fragmented approach posed significant scalability hurdles, making it difficult to build truly interconnected AI systems. MCP directly addresses this challenge by offering a single, standardized protocol designed to replace these disparate integrations.1 This foundational shift promises a more streamlined and reliable method for equipping AI systems with the data they require to function effectively.

The core concept of MCP can be intuitively understood through the analogy of a “USB-C port for AI applications”.2 Just as USB-C provides a universal interface for connecting various devices to peripherals, MCP standardizes the way in which AI applications can access contextual information from a multitude of sources.2 This standardization is pivotal for achieving true interoperability within the AI ecosystem. It allows different AI models and platforms to potentially interact with a wide array of tools and data sources without the need for custom-built integrations for every unique combination. This not only reduces the development overhead but also fosters a more flexible and interconnected AI landscape. Furthermore, MCP is engineered to facilitate secure, two-way communication pathways between data sources and AI-powered tools.1 Given the sensitive nature of much of the data that AI systems might need to access, the emphasis on security is paramount. The protocol is designed with inherent security considerations to ensure that these connections are robust and trustworthy. Ultimately, the primary objective of MCP is to empower advanced AI models to generate better and more relevant responses by effectively bridging their isolation from the external world of data.1 By providing LLMs with the necessary context, MCP aims to unlock their full potential and enable them to contribute more meaningfully to a wider range of applications.

2. Deep Dive into MCP’s Technical Architecture

The Model Context Protocol is built upon a well-defined client-host-server architecture.3 This established architectural pattern promotes a clear separation of responsibilities and facilitates the development of modular and scalable AI systems. Within this framework, LLM applications, such as Anthropic’s Claude Desktop, various Integrated Development Environments (IDEs), and specialized AI tools like Cursor and Cline, function as hosts.3 These hosts initiate connections to MCP servers and manage the lifecycle of client instances. The variety of applications acting as hosts indicates a growing trend of adopting MCP across different categories of AI-powered tools, suggesting a broad potential for integration across diverse AI workflows.

Within the host application reside clients, which are essentially connectors that maintain a one-to-one relationship with MCP servers.3 The host application plays a crucial role in managing these client connections, controlling their permissions and lifecycle, enforcing security policies, handling user authorization, and coordinating the overall integration with the underlying LLM.6 This design implies a level of abstraction and modularity, where a single host can manage multiple concurrent client connections to different servers, enabling complex interactions with various data sources and tools. This allows for flexible and scalable integration of diverse capabilities into a single AI application without requiring monolithic implementations.

The third core component is the server, which are lightweight programs designed to expose specific capabilities through the standardized MCP protocol.3 These servers act as intermediaries or wrappers around external systems, providing a consistent and standardized interface for AI applications to interact with them.6 A key design principle is that MCP servers focus on providing specific, well-defined functionalities, which simplifies their implementation and maintenance.6 Examples of the types of systems that MCP servers can provide access to include databases, Customer Relationship Management (CRM) systems like Salesforce, local file systems, and version control systems such as Git.6 Furthermore, Anthropic has proactively developed and open-sourced a collection of pre-built MCP servers for widely used enterprise systems like Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer.1 The availability of these pre-built connectors, coupled with the potential to connect to a vast array of other systems, underscores the versatility of MCP in accessing diverse data and functionalities, significantly extending the capabilities of LLMs. The lightweight nature and focused capabilities of MCP servers suggest an architectural approach that promotes efficiency and maintainability, potentially contributing to the overall robustness and scalability of AI systems built using this protocol.

Communication between these core components – hosts, clients, and servers – is facilitated by the JSON-RPC 2.0 protocol.6 JSON-RPC is a widely recognized, simple, and transport-agnostic remote procedure call protocol that utilizes JSON for data serialization. The selection of JSON-RPC likely ensures broad interoperability and ease of implementation across various programming languages and platforms due to its inherent simplicity, human-readable format, and extensive library support. This reduces the complexity involved in building MCP components. Within the MCP framework, three primary message types based on the JSON-RPC 2.0 specification are defined: Requests, which are bidirectional messages that expect a response; Responses, which convey the results of a request, either successful data or an error; and Notifications, which are one-way messages that do not require a response.6 These message types enable a range of communication patterns necessary for different types of interactions between clients and servers. For instance, a client might send a Request to a server to retrieve specific data or execute a tool, and the server would respond with a Response containing the requested information or the outcome of the tool execution. Notifications can be used for asynchronous updates or events that the client needs to be aware of. Understanding these fundamental message types is crucial for comprehending the technical mechanisms through which MCP enables interaction between AI applications and external systems.

MCP servers expose their capabilities through three core features: Resources, Prompts, and Tools.6 These serve as the fundamental building blocks for providing context to language models. Resources are designed to expose structured data or content to the LLM.6 Conceptually similar to GET endpoints in a REST API, resources are intended for efficient data retrieval without involving significant computation or causing side effects.8 Access to resources is typically managed through URIs, providing a standardized way to identify and retrieve specific pieces of data.11 By leveraging resources, LLMs can access relevant and up-to-date information from external sources, thereby enriching their knowledge and enabling them to generate more informed and contextually accurate responses. For example, an MCP server might expose a resource that provides the latest stock prices or retrieves customer details from a database.

Prompts in MCP are pre-defined templates or instructions that guide the interactions of language models.6 These reusable templates essentially encode “best practices” for interacting with the server’s capabilities, leading to more effective and predictable LLM behavior.9 Prompts allow developers to structure and control how LLMs utilize the server’s resources and tools, ensuring more consistent and reliable outcomes. For instance, a prompt might instruct the LLM on how to formulate a query to a database resource or how to interpret the output of a specific tool.

Tools are executable functions that enable language models to perform actions or retrieve information from external systems.6 Analogous to POST endpoints in a REST API, tools are designed to execute code or produce side effects, allowing LLMs to actively interact with the external world.8 A key characteristic of tools in MCP is that they are model-controlled, meaning the LLM can discover and invoke them automatically based on its contextual understanding and the user’s prompts.6 Each tool is uniquely identified by a name and includes metadata that describes its schema, specifying the parameters it accepts and the type of output it produces.6 Tools are essential for enabling AI agents to go beyond simply processing information and to actively engage with the real world, performing actions such as sending emails, updating records in a database, triggering automated workflows, or controlling other applications. For example, an MCP server might expose a tool that allows an LLM to schedule a meeting in a calendar application or to deploy a software update.

In addition to the server features, MCP also defines client-side capabilities. One notable feature is Sampling, which clients can offer to servers.7 This advanced capability enables server-initiated agentic behaviors and recursive interactions with LLMs.7 Essentially, a server can request the LLM to perform further reasoning or generate additional content based on the results of a previous interaction. This allows for the creation of more sophisticated AI agents capable of complex, multi-step problem-solving. However, the protocol emphasizes the importance of user control over sampling. Users must explicitly approve any LLM sampling requests and retain control over whether sampling occurs, the specific prompt that will be sent to the LLM, and what results the server is permitted to see.7 This focus on user consent and control underscores the commitment to transparency and safety in these more advanced AI interactions.

3. Exploring the MCP Ecosystem and Available Tools

A robust ecosystem of tools and resources is crucial for the widespread adoption and effective utilization of any technology. The Model Context Protocol benefits from a growing ecosystem that includes an official specification, Software Development Kits (SDKs), reference server implementations, and development tools. The foundation of the MCP ecosystem is the official specification, which is publicly available at spec.modelcontextprotocol.io.7 This document, based on a TypeScript schema, serves as the definitive guide for all aspects of the protocol, ensuring clarity, consistency, and interoperability across different implementations.7 Its public availability is essential for fostering a healthy and thriving community around MCP.

To facilitate the development of MCP-compatible applications, Anthropic provides officially supported SDKs in several popular programming languages, including TypeScript, Python, Java, Kotlin, and C#.1 These SDKs, hosted in the open-source GitHub repository github.com/modelcontextprotocol 12, significantly lower the barrier to entry for developers wanting to integrate MCP into their projects. Furthermore, the community has also contributed SDKs for other languages, such as Rust and Swift 12, expanding the reach and accessibility of the protocol. The involvement of major industry players, such as Microsoft maintaining the C# SDK and collaborations with initiatives like Spring AI for the Java SDK, indicates strong industry interest and support for MCP.

To provide practical guidance and accelerate development, Anthropic has also released an open-source repository of reference implementations for MCP servers at github.com/modelcontextprotocol/servers.1 This repository includes server implementations for popular enterprise systems like Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer.1 These reference servers serve as invaluable examples for developers, demonstrating how to implement MCP servers using the official SDKs and showcasing the protocol’s versatility in connecting to various types of systems. Beyond the officially provided servers, a growing community is actively building and contributing their own MCP server implementations, as evidenced by the expanding list in the modelcontextprotocol/servers repository.12 Examples of community-contributed servers include integrations for AWS Knowledge Base Retrieval, Brave Search, EverArt, Filesystem operations, GitLab, Google Maps, a persistent Memory system, Redis, Sentry, Sequential Thinking, Sqlite databases, and Time and timezone conversions.13 This active community involvement underscores the increasing adoption and interest in MCP and highlights the diverse range of potential applications being explored.

For developers working with MCP, dedicated development and testing tools are also available. The MCP Inspector, found in the github.com/modelcontextprotocol/inspector repository 3, is a valuable visual tool for inspecting and testing MCP servers. This graphical interface simplifies the process of validating server implementations and ensuring they adhere to the protocol specification. Additionally, FastMCP, initially developed as a separate project, has now been integrated into the official Python SDK available on PyPI (pypi.org/project/mcp).8 FastMCP provides a high-level, Pythonic way for Python developers to build MCP servers, offering an intuitive interface and a development mode that includes an interactive web-based MCP Inspector for streamlined testing and debugging.9 The integration of FastMCP into the official Python SDK signifies its importance and provides Python developers with a more efficient and user-friendly approach to MCP server development.

4. Deployment Strategies for MCP

The Model Context Protocol offers flexibility in how its components can be deployed, supporting both traditional HTTP-based setups and zero-HTTP configurations for local integrations. In HTTP-based MCP server-client setups, the protocol leverages standard web technologies for communication. The MCP specification defines “Transports,” and one of the built-in options is Server-Sent Events (SSE) over HTTP.7 SSE is a well-established web standard that enables a server to push real-time data updates to a client over a persistent HTTP connection. This can be particularly advantageous for MCP applications that require continuous streams of data or asynchronous notifications from the server to the client, such as progress updates on long-running tasks or live data feeds.

A significant development in the deployment landscape of MCP is the support for building and deploying remote MCP servers on platforms like Cloudflare.15 This capability extends the reach of MCP beyond local desktop applications, enabling integration with web-based interfaces and mobile apps.15 For these remote deployments, authentication and authorization are handled securely using OAuth, a widely adopted standard for granting applications access to user information or services without requiring password sharing.15 Furthermore, Cloudflare implements additional security measures such as token encryption and the issuance of limited-permission tokens to MCP clients, minimizing the potential impact of compromised credentials.15 The MCP specification itself is also evolving in terms of remote transport, with a move towards Streamable HTTP, which may eventually replace the HTTP+SSE approach.15 Streamable HTTP aims to provide stateless, pure HTTP connections with the option to upgrade to SSE if needed, potentially simplifying remote deployments and improving scalability.

In contrast to HTTP-based setups, MCP also supports zero-HTTP setups, primarily through the use of the stdio (standard input/output) transport.8 Stdio is a fundamental mechanism for inter-process communication in operating systems, allowing applications to communicate through the standard input, output, and error streams. For local integrations and command-line tools, stdio provides a lightweight and efficient way for an application (acting as an MCP client) to interact with an MCP server running on the same machine, without the overhead and complexity associated with HTTP networking. This is particularly well-suited for scenarios where the AI application and the MCP server are tightly coupled or where the integration is primarily command-line driven. A prominent example of a zero-HTTP setup in practice is the AI-powered code editor Cursor, which utilizes the stdio transport for managing MCP servers that run locally on the user’s machine.16 In Cursor, users can configure MCP servers by specifying a shell command that the editor will execute to launch the server process.16 This direct inter-process communication via stdio offers a simple and effective way to extend the capabilities of the code editor with local data and tools. While direct in-process function invocation could theoretically be considered another form of zero-HTTP communication in very tightly integrated scenarios, the standard MCP architecture, which emphasizes a client-server model even for local interactions, typically favors the use of defined transports like stdio for better modularity and maintainability.

5. MCP in the Context of Competing Tool-Calling Approaches

The ability for LLMs to interact with external tools and data is a critical area of development in the field of AI. Consequently, several competing approaches to tool-calling have emerged. To understand the unique position of MCP, it is essential to compare it with other prominent methods, particularly OpenAI function calling and Google’s tool-calling system. A key differentiator for MCP is its nature as an open protocol.1 This contrasts with the proprietary nature of both OpenAI function calling and Google’s system, which are tied to their respective AI models and platforms. The openness of MCP fosters greater interoperability and allows for broader adoption across different AI models and tools.

MCP is specifically designed with the needs of AI in mind, offering a consistent interface based on the concepts of tools and resources.19 This “AI-native” design, coupled with reflection capabilities that allow AI models to dynamically discover the functionalities offered by a server at runtime 19, distinguishes it from more general-purpose API standards like REST, GraphQL, gRPC, and tRPC. While these standards are excellent for inter-service communication, they may not inherently provide the semantic information that an AI model needs to easily understand and utilize the available functions. MCP aims to bridge this gap by providing a standardized way for AI to interact with external capabilities at a higher level of abstraction.

While specific details about Google’s tool-calling system were not prominent in the provided research material, it is understood to be tightly integrated with their Gemini models, allowing them to invoke specific functions to access real-time information and perform actions. Similarly, OpenAI function calling enables developers to define functions that their models (like GPT-3.5 and GPT-4) can call, passing structured data based on the user’s intent. A key difference between MCP and these proprietary approaches lies in the level of control and flexibility offered to developers. MCP’s open standard allows developers to build their own servers and integrate with virtually any system using a variety of programming languages and transport protocols. This contrasts with the more constrained environments of OpenAI and Google, where the tool-calling mechanisms are more closely tied to their specific ecosystems. Furthermore, MCP’s emphasis on security, with detailed guidelines and features like OAuth for remote servers, provides a robust framework for building trustworthy AI integrations. The maturity and breadth of the ecosystem around each approach also differ. While OpenAI and Google have a large existing user base for their models, the MCP ecosystem, though newer, is rapidly growing with contributions from Anthropic, various development tool companies, and the wider community. Finally, MCP supports multiple transport protocols, including stdio for local integrations and SSE/Streamable HTTP for remote communication, offering flexibility in deployment scenarios.

6. Adoption and Implementation of MCP Across Platforms

The Model Context Protocol has seen increasing adoption across various AI platforms, indicating its growing importance as a standard for AI integration. As the originator of MCP, Anthropic has deeply integrated the protocol into its Claude ecosystem.1 The Claude Desktop application provides native support for connecting to local MCP servers, allowing users to seamlessly extend the capabilities of their AI assistant with external data and tools.1 Notably, Claude 3.5 Sonnet has been highlighted for its proficiency in rapidly building MCP server implementations, suggesting that Anthropic is leveraging its own models to facilitate the creation of MCP integrations.1 Furthermore, Claude for Work customers can readily test MCP servers locally, enabling them to connect Claude to their internal systems and datasets.1 To simplify the user experience, pre-built MCP servers for popular services can be easily installed directly through the Claude Desktop application.1

Cursor, an AI-powered code editor, has also strongly embraced MCP, viewing it as a fundamental “plugin system” for extending the capabilities of its AI Agent.5 This integration allows developers using Cursor to connect their AI assistant to project-specific data, code repositories, and other development tools, significantly enhancing their coding workflow. Cursor supports both stdio and SSE transport protocols for connecting to MCP servers, providing flexibility for different deployment scenarios.16 The editor features a dedicated MCP settings page, offering a user-friendly interface for managing and monitoring connected MCP servers.16 The active engagement of the Cursor community in discussing and exploring the potential of MCP further underscores its importance within this platform.21 Currently, Cursor’s implementation primarily focuses on supporting MCP tools, with support for resources still under development.16 There is also a current limitation on the number of tools that Cursor will actively utilize 16, and users should be aware that MCP server functionality might be limited when accessing Cursor over SSH or other remote development environments.16

Another significant platform adopting MCP is Cline, a VS Code extension that integrates with the Anthropic API.18 Cline provides an MCP Marketplace, a curated collection of MCP servers that users can easily discover and install with a single click.23 This marketplace model greatly simplifies the process of integrating external capabilities into the Cline AI assistant. Cline also employs a structured approach to MCP server development, utilizing a .clinerules file to guide developers and enforce best practices.24 The cross-platform support for connecting to services like Supabase using MCP with Cursor, Windsurf (Codium), Cline, and Claude (desktop and code) highlights the growing reach and interoperability facilitated by the protocol.18

Beyond these key platforms, several other development tool companies, including Zed, Replit, Codeium, and Sourcegraph, are actively working on integrating MCP into their offerings to enhance their AI-powered features.1 Furthermore, Microsoft has recently announced the inclusion of MCP in Copilot Studio, indicating a broader industry recognition of its value.5 This widespread adoption across diverse platforms signifies a strong trend towards establishing MCP as a fundamental standard for connecting AI with the external world.

7. Security, Trust, and Safety Considerations in MCP Implementations

Given the powerful capabilities enabled by connecting AI systems to external data and tools, security, trust, and safety are paramount considerations in the design and implementation of the Model Context Protocol. The MCP specification dedicates a significant section to these aspects, emphasizing several key principles.7 User consent and control are central, requiring that users must explicitly consent to all data access and operations and retain control over what data is shared and what actions are taken.7 Implementors are advised to provide clear user interfaces for reviewing and authorizing activities. Data privacy is another critical principle, mandating that hosts must obtain explicit user consent before exposing user data to servers and must not transmit resource data elsewhere without this consent.7 User data should be protected with appropriate access controls.

Tool safety is also a major concern, as tools represent arbitrary code execution paths.7 The specification advises that descriptions of tool behavior should be considered untrusted unless obtained from a trusted server. Explicit user consent must be obtained before invoking any tool, and users should understand the tool’s function before authorizing its use. Finally, the specification addresses LLM sampling controls, requiring that users must explicitly approve any LLM sampling requests and should control whether sampling occurs, the prompt that will be sent, and what results the server can see.7 Notably, the protocol intentionally limits server visibility into prompts to protect user privacy.

The MCP specification strongly recommends several implementation guidelines to ensure security and trust.7 These include building robust consent and authorization flows into applications, providing clear documentation of security implications to users, implementing appropriate access controls and data protections, following general security best practices in integrations, and carefully considering privacy implications in feature designs. For remote MCP servers deployed on platforms like Cloudflare, secure authentication is achieved through OAuth.15 Cloudflare also enhances security by encrypting tokens and issuing limited-permission tokens to MCP clients.15 The intentional limitation of server visibility into user prompts within the protocol itself further underscores the commitment to protecting user privacy.7 These comprehensive security considerations highlight the importance placed on building a safe and trustworthy ecosystem around the Model Context Protocol.

8. Real-World Use Cases and the Future of MCP

The practical utility of the Model Context Protocol is evidenced by its adoption by early pioneers such as Block and Apollo, who have integrated MCP into their systems.1 While specific details of these integrations were not extensively covered in the provided material, their adoption by established organizations highlights the real-world value proposition of MCP. The research material also provides a range of concrete examples illustrating the diverse use cases enabled by MCP servers. These include the ability to fetch pull request details from GitHub, analyze code changes, generate pull request review summaries, and save these reviews to Notion.25 MCP also facilitates interaction with databases, as demonstrated by integrations with PostgreSQL 16 and Upstash 17, allowing AI to query and manipulate data through natural language. Other use cases include sending emails via Resend’s API 20, reading and searching through notes in Obsidian 20, and even controlling complex software like Blender for animation purposes.17 These varied examples underscore the flexibility and broad applicability of MCP in connecting AI with a wide spectrum of external systems and functionalities.

Looking towards the future, the development of the MCP Marketplace by Cline 23 signals a significant step towards easier discovery and integration of MCP servers. This marketplace model has the potential to drive rapid expansion of the MCP ecosystem by simplifying the process for users to find and utilize relevant integrations. The analogy of MCP as the “REST API of LLM apps” 22 and the “USB-C of AI integrations” 2 suggests a future where MCP becomes a foundational standard for connecting AI with the external world, enabling a more interconnected and capable AI landscape. The ongoing evolution of the protocol’s transport mechanisms, such as the shift towards Streamable HTTP for remote communication 15, demonstrates a commitment to adapting and improving MCP to meet the evolving needs of the AI community and to support diverse deployment scenarios.

9. Conclusion

The Model Context Protocol represents a significant advancement in the quest to create more capable and integrated AI systems. By providing an open and standardized framework for connecting LLMs with external data sources and tools, MCP addresses a fundamental limitation of current AI technology. Its well-defined architecture, comprehensive security considerations, growing ecosystem of tools and SDKs, and increasing adoption across various platforms position it as a key enabler for the next generation of AI applications. The diverse range of real-world use cases already emerging highlights the practical value and versatility of MCP in bridging the gap between the potential of advanced AI models and the complexities of the real world. As the ecosystem continues to mature and evolve, the Model Context Protocol holds the promise of unlocking new levels of AI capability and driving innovation across a wide range of industries.

Works cited

1. Introducing the Model Context Protocol - Anthropic, accessed March 31, 2025, https://www.anthropic.com/news/model-context-protocol
2. Model Context Protocol (MCP) - Anthropic API, accessed March 31, 2025, https://docs.anthropic.com/en/docs/agents-and-tools/mcp
3. Model Context Protocol: Introduction, accessed March 31, 2025, https://modelcontextprotocol.io/introduction
4. Model context protocol (MCP) - OpenAI Agents SDK, accessed March 31, 2025, https://openai.github.io/openai-agents-python/mcp/
5. What the heck is Model Context Protocol (MCP)? And why is everybody talking about it?, accessed March 31, 2025, https://pieces.app/blog/mcp
6. The Model Context Protocol (MCP) — A Complete Tutorial | by Dr. Nimrita Koul - Medium, accessed March 31, 2025, https://medium.com/@nimritakoul01/the-model-context-protocol-mcp-a-complete-tutorial-a3abe8a7f4ef
7. Model Context Protocol specification, accessed March 31, 2025, https://spec.modelcontextprotocol.io/specification/2025-03-26/
8. mcp · PyPI, accessed March 31, 2025, https://pypi.org/project/mcp/
9. jlowin/fastmcp: The fast, Pythonic way to build Model … - GitHub, accessed March 31, 2025, https://github.com/jlowin/fastmcp
10. The official Typescript SDK for Model Context Protocol servers and clients - GitHub, accessed March 31, 2025, https://github.com/modelcontextprotocol/typescript-sdk
11. Model Context Protocol (MCP) :: Spring AI Reference, accessed March 31, 2025, https://docs.spring.io/spring-ai/reference/api/mcp/mcp-overview.html
12. Model Context Protocol - GitHub, accessed March 31, 2025, https://github.com/modelcontextprotocol
13. modelcontextprotocol/servers: Model Context Protocol Servers - GitHub, accessed March 31, 2025, https://github.com/modelcontextprotocol/servers
14. Transports - Model Context Protocol, accessed March 31, 2025, https://modelcontextprotocol.io/docs/concepts/transports
15. Build and deploy Remote Model Context Protocol (MCP) servers to Cloudflare, accessed March 31, 2025, https://blog.cloudflare.com/remote-model-context-protocol-servers-mcp/
16. Model Context Protocol - Cursor, accessed March 31, 2025, https://docs.cursor.com/context/model-context-protocol
17. Model Context Protocol (MCP): Build Your Own in 5 Minutes | Upstash Blog, accessed March 31, 2025, https://upstash.com/blog/build-your-own-mcp
18. Model context protocol (MCP) | Supabase Docs, accessed March 31, 2025, https://supabase.com/docs/guides/getting-started/mcp
19. Model Context Protocol (MCP): Enabling More Capable AI - Medium, accessed March 31, 2025, https://medium.com/zero-to/model-context-protocol-mcp-enabling-more-capable-ai-a80f0492cfb4
20. MCP Servers for Cursor - Cursor Directory, accessed March 31, 2025, https://cursor.directory/mcp
21. Cursor Team Take note *Model Context protocol) - Discussion, accessed March 31, 2025, https://forum.cursor.com/t/cursor-team-take-note-model-context-protocol/29866
22. I still have no idea what MCP (Model Context Protocol) is : r/Anthropic - Reddit, accessed March 31, 2025, https://www.reddit.com/r/Anthropic/comments/1hmqdtc/i_still_have_no_idea_what_mcp_model_context/
23. This is the official repository for submitting MCP servers to be included in Cline’s MCP Marketplace. If you’ve built an MCP server and want it to be discoverable and easily installable by millions of developers using Cline, submit your server here. - GitHub, accessed March 31, 2025, https://github.com/cline/mcp-marketplace
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25. Model Context Protocol (MCP): A Guide With Demo Project - DataCamp, accessed March 31, 2025, https://www.datacamp.com/tutorial/mcp-model-context-protocol

The Little Deep research Engine That Could: ChatGPT

I start the deep research horserace in the browsers with ChatGPT 4.5 Deep first because it usually finishes last, but I’m not sure that’s a bad thing. You wouldn’t want your researcher giving up before it was done, would you? It’s just blatantly obvious OpenAI’s infrastructure rented from Microsoft is not the same as actually being Google. Video-game tropes endlessly come to mind. Gemini is the Lightning Bruiser while ChatGPT is the Determinator – the scrappy beginner who won’t give up. You can tell by its language… Awesome!

ChatGPT 4.5 Deep research: Awesome. I’ll dig deep into the current state of the Model Context Protocol (MCP), especially in the context of how AI-assisted development environments like Claude Desktop, Cursor AI, and others are using it. I’ll map out the components, plugin ecosystem, and how local clients like your Ollama-powered Pipulate can adopt MCP.

I’ll include:

• A breakdown of MCP: its format, discovery mechanisms, and schema definition
• What’s officially documented vs what’s de facto implemented
• How MCP clients are structured and how they call MCP servers
• Plugin marketplace ecosystem (especially in Claude and Cursor)
• Viable no-HTTP options for embedding tool-calling locally
• Best practices for embedding MCP into a plugin-based app with linear workflows and CRUD apps

I’ll get back to you shortly with a full breakdown and annotated action steps you can use to start wiring MCP into your codebase.

Model Context Protocol (MCP): Architecture, Adoption, and Integration

Model Context Protocol (MCP) is an open standard (introduced by Anthropic in late 2024) that connects large language models to external data sources and tools. MCP defines a client-server architecture using a JSON-RPC 2.0 message protocol. In this architecture, MCP servers expose specific capabilities (data or actions), and MCP clients (built into host applications like IDEs or chatbots) connect to those servers to let an AI model use the capabilities. Below, we break down MCP’s structure and schema, current industry adoption, the plugin ecosystem, zero-HTTP (in-process) transport options, and how to embed MCP support into a local app like Pipulate.

1. What Exactly is MCP?

MCP is to AI what USB is to peripherals: it standardizes how LLM-powered apps interface with external systems (Introduction - Model Context Protocol). Instead of custom integrations for each tool or database, MCP offers a uniform protocol. Key aspects include:

• Protocol Structure: MCP uses JSON-RPC 2.0 messages for all communication between clients and servers. This means requests, responses, and notifications are all JSON objects following JSON-RPC format (with fields like jsonrpc, id, method, etc.). The protocol is stateful – clients maintain persistent connections to servers, allowing ongoing interactions (similar to how an IDE might keep a language server running) (Introduction - Model Context Protocol).

• Roles: An MCP Host (e.g. Claude Desktop or an AI IDE) contains an MCP client component that manages connections to one or more MCP servers (Introduction - Model Context Protocol). The host’s LLM (the AI model) is essentially the “brain” that decides when to use a tool, while the client and server handle the mechanics of executing that tool call and returning results. This separation is analogous to the Language Server Protocol (LSP) in developer tools – where the editor (host) communicates with language servers to provide features like autocomplete. In MCP’s case, the host (LLM app) communicates with context/tool servers to provide the LLM with extra capabilities.

• Schema Definition: MCP formally defines message schemas and data structures in its specification (the authoritative source is a TypeScript schema file (Model Context Protocol specification – Model Context Protocol Specification)). Tools and other capabilities are described in JSON Schema. For example, each Tool exposed by a server has an inputSchema (a JSON Schema for expected parameters), a name, and a description. The use of JSON Schema means both humans and the LLM can understand what inputs a tool expects and validate them. The Tool Result returned by a server includes a content payload, which can be text, images, or other media types, and a flag indicating if it’s an error.

• Discovery Mechanisms: Discovery in MCP happens at two levels:
  1. Discovering Servers: Currently, finding available MCP servers is done via configuration or emerging registries. There isn’t yet an automatic network discovery in the protocol. Instead, users or developers point the MCP client to server endpoints (or commands to run them). For instance, Claude Desktop reads a config file (claude_desktop_config.json) where you list servers to use (For Claude Desktop Users - Model Context Protocol). Tools like Windsurf (Codeium’s AI IDE) have UI settings to add MCP servers by URL or command. A community-driven package index (e.g. mcp-get or “MCP Server Directory”) is also evolving to help discover servers. In the future, Anthropic has signaled an official registry to streamline this, analogous to a plugin marketplace.
  2. Discovering Tools (at runtime): Once a client connects to a server, it must learn what tools or data that server offers. MCP defines a tools/list request that the client sends over JSON-RPC to get a list of available tools and their schemas. The server responds with an array of tool definitions. The client can call this periodically or rely on a tools/list_changed notification if the server supports it (indicated by a listChanged flag in capabilities). Similarly, for other feature types: there are analogous list/look-up methods for Resources (contextual data items) and Prompts (predefined prompt templates or workflows), if those features are supported by the server.

• Tool Invocation (Function Calls): To invoke a tool, the client sends a tools/call request with the tool name and JSON arguments. The server executes the action and returns a result (often a text snippet or data blob) that the client can relay back to the LLM. Crucially, MCP is two-way: not only can the LLM (via the client) call the server’s tools, but some servers can also initiate requests back to the client in special cases. For example, an advanced server can use the Sampling feature to ask the client’s LLM to perform a sub-completion (essentially letting the server query the LLM to elaborate or make a decision). This is akin to the server saying “I need the model to help with this part of the task,” and it’s done in a controlled, user-consented way.

• MCP Workflow: The typical flow of an MCP interaction is:
  1. Connection & Capability Negotiation: The host (client) starts the connection, often by launching the server (locally) or opening a socket to it. They exchange an initialize message where the client and server advertise their supported features (e.g. server says “I have tools and resources”, client says “I can handle roots and sampling” if applicable). This is similar to handshakes in LSP.
  2. Tool/Resource Discovery: The client calls tools/list (and resources/list if using contextual data) to get the inventory of what’s available.
  3. LLM Prompting: The client then incorporates the available tools into the LLM’s context. How exactly? The MCP spec leaves it open, but in practice the host might inject a system prompt listing the tools and how to invoke them, or use the model’s function-calling API. The idea is the model “knows” what tools exist and can decide to use them. For example, Claude Desktop automatically allows Claude (the model) to choose tools; Cursor IDE uses OpenAI’s function calling by mapping MCP tools to function-callable definitions, etc.
  4. Model Decides to Use Tool: During a conversation or task, if the model determines a tool is needed, it will respond (in whatever format the host expects) indicating a tool action. In Claude Desktop, the model might directly say (in hidden output) which tool to run; with OpenAI models it could return a function call JSON. Either way, the MCP client intercepts this.
  5. Tool Execution: The client receives the model’s tool request and issues a tools/call to the server. The server executes the operation (e.g. query a database, fetch a file, run a command) and returns the result data or error.
  6. Return Results to LLM: The client takes the server’s response and feeds it back to the model. Again, the mechanism can vary: it might inject the result as a system message or as the next user prompt. In many implementations, the model is asked to “continue the answer incorporating this result,” enabling a final answer to the user. This closed-loop continues until the model finishes the task.

Example: Suppose an MCP server provides a get_weather tool. The user asks, “What’s the weather in New York?”. The flow would be: The MCP client lists tools (sees get_weather exists) → The model, seeing this tool is available, responds with an intention to use it → The client sends tools/call to the server with {"name": "get_weather", "arguments": {"location": "New York"}} → The server returns weather info text → The client gives that info to the model which then replies to the user with the weather report, possibly quoting the fetched data. All of this happens seamlessly, with the user only seeing the end result after approving any tool use.

• Prompts and Resources: Beyond tools, MCP servers can also supply Resources (like documents, database entries, code files) that serve as context for the model, and Prompts which are reusable prompt templates or multi-step workflows. For instance, a “filesystem” server could expose a text file as a Resource; a “git” server might expose a commit history as a Resource or offer a Prompt workflow for summarizing recent changes. These are accessed via similar JSON-RPC methods (resources/list, resources/lookup, etc.). The LLM might not call these directly like tools; instead, the client might pre-fetch relevant resources and include them in the conversation. For example, Continue (an open-source IDE assistant) uses the Resources feature to attach code context from files to the prompt. Tools, Prompts, and Resources are the three pillars of MCP’s feature set.

• Security Model: MCP is powerful – tools can be arbitrary code execution – so the protocol emphasizes a human-in-the-loop and explicit consent model. Clients (hosts) are expected to ask the user for permission before running potentially dangerous tool actions, and to clearly show what data is being accessed. For example, Claude Desktop will prompt “The assistant wants to run a terminal command rm -rf /photos. Allow?” and won’t execute unless you approve. The spec outlines principles: user consent, data privacy, tool safety, etc., and leaves implementation details to the client. For local integrations like Pipulate, this means you should build confirmation dialogs or other safeguards around tool usage.

In summary, MCP provides a structured way for LLMs to use tools: a JSON-RPC pipeline for listing and calling functions with well-defined schemas, plus a framework for sharing data context. It’s designed to be model-agnostic – any LLM (cloud or local) that can follow the right prompting or function-call format can leverage MCP servers to gain new abilities (Using the Model Context Protocol (MCP) With a Local LLM | by Ashraff Hathibelagal | Predict | Mar, 2025 | Medium).

2. Current State of MCP Adoption

Since its introduction, MCP has rapidly gained adoption in AI coding and assistant tools. Anthropic’s own Claude 3.5/3.7 models and the Claude Desktop app set the reference implementation, and many others have followed. Here’s the current landscape of MCP support (as of early 2025):

• Claude Desktop (Anthropic) – Full reference client. Claude Desktop (the downloadable app) was launched with native MCP support and is considered the gold standard. It supports all major MCP features: Tools, Resources (e.g. attaching local files), and Prompts. Users can configure Claude Desktop to run local servers by editing the JSON config file as mentioned earlier. Claude Desktop will automatically spawn those servers as subprocesses on launch and allow Claude (the model) to call them. Notably, this is local-first (no data leaves your machine unless the server itself calls an external API). Claude.ai (web version) does not support MCP yet – this limitation underscores MCP’s initial focus on local integration. Anthropic also open-sourced ~10 official servers (e.g. for Slack, GitHub, filesystems, web browsing via Puppeteer, etc.) and highlighted early enterprise adopters like Block using MCP.

• Cursor AI (Cursor IDE) – AI coding IDE with MCP tools. Cursor (an AI-assisted code editor) added MCP support to its “Agent” feature. It allows integration of external tools via MCP, primarily focusing on Tools (not Resources) (Example Clients - Model Context Protocol). For example, you can connect Cursor’s agent to a “Zapier” MCP server to get access to thousands of app actions, or to a database MCP server. Cursor’s documentation states you can connect to 100+ MCP servers in minutes. Under the hood, Cursor likely maps MCP tools to the OpenAI function-calling interface (since Cursor can use models like GPT-4). This means it reads the tools/list info and then dynamically registers those as functions for the model. This approach leverages GPT-4’s strong function-calling ability while adhering to the MCP standard for the server side. As a result, even though Cursor’s UI treats them as “MCP tools”, the model itself is just doing function calls. This is an example of bridging official spec and emergent practice: the MCP spec doesn’t dictate how an OpenAI model should be used, but developers found a way to combine them. Cursor’s support is growing; community forums show many are using it to extend the IDE with custom commands via MCP.

• Cline (VS Code Extension) – Autonomous coding agent that uses Claude or other models. Cline is an open-source VS Code extension that implements an agent (“Cline”) capable of complex coding tasks. It supports MCP for both Tools and Resources. Cline’s documentation notes that it “can even use MCP to create new tools and extend his own capabilities”. In practice, this means Cline’s agent can, if needed, spin up or utilize an MCP server. Remarkably, the phrasing suggests Cline might programmatically generate an MCP server (for example, writing a small server script on the fly) if it decides a new function is needed. This is experimental, but it shows how MCP is enabling self-extension: an AI agent adding capabilities at runtime via the standard interface. Cline’s primary model is Claude 3.7 (which is well-suited for tool use), and it emphasizes human approval for each action, aligning with MCP’s safety guidelines.

• Continue (Open-source IDE agent) – Continue is another IDE assistant (for VS Code and others). It reportedly has full MCP support (tools, prompts, resources). This means it can use MCP to fetch context (e.g. load project files as Resources) and execute Tools. Continue’s integration likely uses open models or OpenAI, similar to Cursor. By supporting Prompts, it can incorporate workflow templates from servers – for example, a “code review” prompt provided by a server to guide the model.

• Windsurf (Codeium’s Cascade) – AI-powered code editor. Codeium’s Windsurf (previously codenamed Cascade) has integrated MCP to bring in more tools for its agent called “Cascade”. Windsurf initially supported just local tools, but recent updates (Wave 3 and 4) mention “MCP support: bring in more tools for Cascade to access” and improved MCP discoverability. They’re making it easier for users to identify and connect useful MCP servers, possibly via a UI or curated list. Windsurf appears focused on Tools as well, allowing things like database queries (Neon DB example) to be done through MCP. Codeium’s integration of MCP indicates that even companies with their own AI solutions see value in a common protocol for extending with third-party capabilities.

• Other Notable Adopters: Many other projects are adding MCP:

  • Fast-Agent (by community) claims “full multimodal MCP support” including the experimental Sampling feature (meaning the server can ask the agent to do sub-tasks via the LLM – not common yet).
  • Microsoft has shown interest: Copilot Studio lists support for MCP tools, and there’s an emerging pattern in the VS Code ecosystem to incorporate MCP servers for things like browsing or terminal control in a standardized way. Microsoft’s “GenAI Script” and OpenSumi IDE also have basic tool support.
  • OpenAI’s perspective: OpenAI recently added MCP support in their experimental Agents SDK, indicating a convergence of ideas. They had their own plugin system (the .well-known/ai-plugin.json manifest with OpenAPI specs), but MCP is more general and multi-directional. One advantage noted is that MCP’s Streamable HTTP transport no longer requires an always-on WebSocket for remote servers – it can work with simple HTTP requests and server-sent events for streaming. This shows openAI acknowledging MCP as an industry-wide effort, not just an Anthropic project.

Overall, Claude Desktop remains the most fully-featured MCP client (covering all spec aspects except Sampling) and is often used as the testbed for new servers. Cursor, Cline, Windsurf, Continue, and others focus on the Tools aspect, integrating with coding workflows. The community is rapidly iterating: since MCP is open, independent developers have built clients for Emacs, chat UIs like LibreChat, and even a command-line MCP client for testing.

Official vs. Reverse-Engineered: Because MCP was open-sourced from the start (with documentation and SDKs), there’s less need for reverse-engineering. Early on (late 2024), before documentation was thorough, projects like Cursor and Cline may have experimented with partial info from Claude Desktop’s behavior. But now the official spec and SDKs (Python, TypeScript, Java, etc.) (Model Context Protocol · GitHub)are available, and Anthropic runs an official community forum. That said, some practices are emergent: for example, how to best prompt the model with available tools is learned by trial – developers share prompt strategies on forums and Reddit. Another emergent practice is mapping MCP to other frameworks (like converting MCP tool schema to an OpenAPI spec for OpenAI function calling). These are not “official” but are common in the community, effectively bridging MCP with other AI ecosystems. The MCP spec is still evolving too (revisions are numbered by date), often informed by community feedback. Features like Roots (scoping what part of a filesystem or project a server should operate on) exist in spec but few clients support them yet – they may gain traction as use-cases arise. Sampling, as mentioned, is powerful but not yet enabled in Claude Desktop due to the need for careful UX, so only experimental clients play with it. In summary, the core protocol is documented, but how it’s used in practice is being refined through real-world usage.

3. MCP Plugin Ecosystem and Marketplace

One of MCP’s promises is a universal plugin ecosystem for AI assistants. An “MCP server” is essentially a plugin that any MCP-compatible client can use. The ecosystem is growing quickly:

• Official and Community Servers: Anthropic kickstarted a repository of open-source MCP servers. These include connectors to common tools: e.g. Filesystem (read/write local files), Terminal/Process execution, Web Browser (controlled browsing), GitHub (repo and issue queries), Slack (fetch messages), Google Drive, Notion, Postgres DB, etc. Many are simple and meant as reference implementations for others to build on. Beyond Anthropic’s list, the community has contributed dozens more:
  • “Awesome MCP Servers” lists on GitHub catalog servers for everything from Jira to Spotify.
  • There are servers like Zapier MCP (giving access to 8000+ Zapier actions via one integration), BrowserTools (a headless browser), DuckDB (SQL analytics engine in-process), Obsidian (note-taking), and even game or creative tool integrations. Essentially, if an API or system exists, someone can wrap it in an MCP server to let AI agents use it.
• Marketplace and Discovery: Because there are so many servers, the idea of a marketplace or registry is emerging. Currently:
  • Anthropic has hinted at an official MCP registry (a centralized directory to discover and perhaps auto-install servers). This would function like an app store for AI plugins, ensuring security and quality. Some in the community are wary of centralization, wanting to ensure it remains open, but a registry could address trust and versioning issues (similar to npm or PyPI, but for AI tools).
  • Meanwhile, independent efforts have stepped in. Glama.ai offers an MCP Server Directory that automatically introspects servers to list their tools, required config, and check for vulnerabilities. This helps users compare and choose servers confidently.
  • MCP-Get (mcp-get.com) is another project aiming to be a package manager for MCP servers. It envisions a one-stop site to find and install servers (“the simplest way to discover, track, and manage MCP servers”). Though still in early stages, you might soon be able to do something like mcp-get install server-filesystem to fetch and run a server in one command.
  • Some MCP clients have built-in marketplaces: for instance, Cursor’s website has a “Cursor Directory” listing MCP servers known to work well with Cursor (with one-click integration for marketing tools, database tools, etc.), and Windsurf’s UI improvements suggest a more seamless server import experience.

• Role of Plugins vs. Clients: It’s important to clarify that MCP servers (plugins) are external programs or services. They don’t “extend the MCP client’s code” directly; rather, they expose functionality over the protocol that the client can tap into. For example, a “Google Calendar” MCP server might expose a list_events tool and a add_event tool. An MCP client (say an AI scheduling assistant) that connects to that server can now, through the LLM, list or add calendar events. So the plugin is running separately (maybe as a local process or remote API), and the MCP client is the intermediary facilitating the conversation between the LLM and that plugin.

• What Servers Expose: Typically, an MCP server will declare one or more of:
  • Tools: Most common – functions the model can call. These could be actions (like “send email”, “run SQL query”) or computations (like “solve math equation”). Each tool comes with a name, description, and JSON schema for inputs. Many servers focus solely on tools.
  • Resources: Some servers also provide data context. For example, a Git MCP server might have a resource list of recent commits or file diffs; a Confluence MCP server could list knowledge base articles. Resources can often be large text blobs that the client might insert into the prompt to give the model more information. They’re typically read-only or domain data that complements the tools.
  • Prompts: A smaller subset offer prompt templates or workflows. For instance, an “Agent Workflow” server could provide a series of steps to accomplish a task that the model can follow. Or a “Code Review” server might include a prompt outline for reviewing code changes (with placeholders for code diff, etc.). These are more meta, guiding the LLM’s behavior rather than performing actions in the world.

• Ecosystem Maturity: While the idea of a plugin marketplace is in its infancy, the available MCP servers already cover a wide range, effectively functioning as a plugin ecosystem. The benefit is interoperability: a developer can write one MCP server and it can be used by multiple clients (Claude Desktop, Cursor, Pipulate, etc.), rather than writing separate plugins for each platform. This has accelerated the growth of available integrations. Many early servers are experimental, but we’re seeing movement towards production-grade ones. For example, companies like Block and Replit have been developing MCP integrations for their contexts, and Cloudflare even blogged about deploying remote MCP servers on their edge for performance and scalability.

• Standards and Documentation: Official documentation for server developers exists (including tutorials and an “MCP Inspector” tool to test servers interactively). However, not everything is fully standardized yet. Certain conventions (like how to authenticate to an API inside an MCP server, or how to handle long-running tool calls) are being worked out. Some “best practices” guides are community-driven (e.g. security hardening for servers, or how to write clear tool descriptions so models use them effectively). The ecosystem is vibrant, but developers should stay updated with forums and the MCP spec roadmap for changes.

4. Zero-HTTP MCP: In-Process and Low-Overhead Transports

MCP’s default transports use processes or HTTP, but it’s flexible by design. In contexts like a local app where performance and simplicity matter, you can implement “zero-HTTP” communication, meaning not relying on conventional HTTP servers at all. Options include:

• Standard I/O (STDIO) Transport: MCP natively supports using standard input/output streams as a transport. This is how many language servers work and is ideal for local, in-process or spawned-process communication. For example, a host application can launch an MCP server as a child process and communicate via its stdin/stdout. The JSON-RPC messages flow over this pipe with minimal overhead (no HTTP parsing beyond JSON, no sockets). The MCP TypeScript and Python SDKs make this easy – you can instantiate a StdioServerTransport on the server side and have the client connect to it. This essentially treats the server as an in-process plugin from the user’s perspective. Many clients (Claude Desktop, etc.) use stdio for local servers by default. This avoids any HTTP stack, hence “zero-HTTP.” The trade-off is that the server is a local process, so you don’t have network latency or HTTP overhead, but you also can’t easily host it remotely (stdio is inherently local).

• Unix Domain Sockets or Named Pipes: These are not built into MCP out-of-the-box, but custom transports are allowed. You could implement a transport over a Unix socket (or Windows named pipe) which avoids the TCP layer and uses OS IPC for efficiency. Practically, the performance gain over localhost TCP is minor for most use cases, but domain sockets can offer security (socket file permissions) and slight overhead reduction. A custom transport would simply need to read/write JSON strings from the socket and feed them to MCP’s Protocol handler. This is feasible if, for example, you want a separate process but don’t want to expose an HTTP port.

• In-Process Function Calls: The most extreme “zero-HTTP” integration is to run the server in the same process as the client (essentially linking it as a library). Because MCP is just a protocol, if you control both client and server code (e.g. both are in Python or you can embed one in the other), you could bypass serialization entirely. For instance, using the Python MCP SDK, you can register tool functions and call them directly via the MCP client API without ever marshaling to JSON. The Spring AI SDK for Java describes an “in-process based transport” that simply calls server logic directly. However, doing this means losing isolation – a bug in the server could crash your app, or a malicious server plugin would have direct access to your app’s memory/state. Security and stability trade-offs are significant here: normally, MCP servers are meant to be sandboxed to some degree (even running a local server on stdio gives you the option to kill it if it misbehaves and it can’t directly interfere with the client’s memory). In-process integration should only be done for fully trusted, lightweight tools where the function call overhead is critical to eliminate (which is rare – JSON serialization is usually not the bottleneck compared to what the tool itself is doing like I/O or API calls).

• Server-Sent Events (SSE) Transport: On the other end of the spectrum, if you do go with HTTP, MCP chose a simple HTTP + SSE mechanism rather than complex WebSockets. The SSE transport (supported in the TypeScript SDK) uses an HTTP POST endpoint for requests from client->server and an SSE stream for server->client messages, enabling streaming responses. This avoids needing a bidirectional WebSocket connection and fits easily into serverless or restricted environments. Streamable HTTP was a recent addition that allows remote servers to not require a persistent connection. So even if you use HTTP, it’s optimized for ease and low overhead (SSE is essentially just an HTTP response that stays open). In a local context, SSE is usually unnecessary overhead compared to stdio, but it’s there if needed (for example, if your local UI is web-based and you prefer communicating over localhost HTTP between a web front-end and a Python back-end MCP server).

Performance Considerations: For a local application like Pipulate, using STDIO or direct function calls will offer the best performance (microsecond latency and no networking). JSON serialization cost is modest, but if you call extremely frequently or large payloads, binary or in-memory could be faster. The MCP spec allows custom transports, so one could even imagine a shared-memory or gRPC transport – but that’s likely overkill. Start with STDIO, which is proven and simple.

Security Trade-offs: Keeping an MCP server as a separate process (communicating via stdio or sockets) is generally safer. The OS can enforce permissions, and the host app can terminate the server if needed. If you go in-process, you must treat the MCP server code as part of your trusted codebase. A compromise could be to run certain “safe” tools in-process (like a simple math or string utility) while using separate processes for riskier ones (like shell command execution). MCP’s design is flexible enough to mix – you could have multiple servers, some over stdio (separate processes) and maybe a special internal one via a direct call.

Zero-HTTP Summary: In essence, MCP does not force a web server approach. It’s transport-agnostic: “use any transport you want as long as it uses JSON-RPC as the payload.” This is great for local apps. The simplest is to spawn a process and use pipes (stdio), achieving near in-process speeds with isolation. Many reference servers (especially those written in Node or Python) support running in this mode out-of-the-box (Claude Desktop’s config, for example, just specifies a command and it uses stdio implicitly (For Claude Desktop Users - Model Context Protocol)). So Pipulate can leverage this to avoid any HTTP overhead and keep everything offline.

5. Embedding MCP Support in Pipulate (Local LLM Web App)

Pipulate is described as a local web application powered by Ollama-based LLMs, with its own plugin architecture for things like CRUD database apps and notebook-style workflows. Integrating MCP into Pipulate will allow its LLM to perform a wide range of tasks by tapping into MCP servers. Here’s a plan and best practices for adding an MCP client in Pipulate:

a. Leverage Pipulate’s Existing Plugin System

First, examine how Pipulate’s plugin architecture works for its current features (CRUD, notebook operations). Ideally, you want MCP to complement this, not conflict. If Pipulate already has a notion of “plugins” that can execute certain actions, you might implement MCP support as another plugin module:

• This MCP plugin would handle discovering and managing MCP server connections, and routing tool calls.
• It can act as an abstraction layer: to the rest of Pipulate, it might present MCP tools as just additional internal plugin functions. But under the hood, those calls go out via the MCP client to the external server.

However, since MCP is agentic (the LLM decides when to use tools), integration is a bit more involved than a normal plugin triggered by a user click. It ties into the LLM inference loop.

b. Incorporate MCP Client at Application Startup

Identify a place in Pipulate’s startup where you can initialize connections to MCP servers. Possible steps:

• Select MCP Servers to Use: You might allow configuration (just like Claude’s JSON file) where users list which MCP servers (and their parameters) they want Pipulate to use. For example, in a config: servers: [{ name: "filesystem", command: "npx @modelcontextprotocol/server-filesystem /path1 /path2" }, { name: "postgres", command: "mcp-postgres", env: {...} }]. Pipulate can read this and attempt to start/connect to each server.
• Use MCP SDK or Implement Client: Easiest is to use the official Python or TypeScript MCP SDK (depending on what Pipulate is built in). These SDKs handle the JSON-RPC messaging, multi-threading, etc. For instance, the Python SDK can spawn a process and connect via stdio in a few lines. If Pipulate is Node/TypeScript, the TS SDK will do similarly. The SDK will give you a client object representing the connection.
• Connection Lifecycle: For each configured server, launch it (if it’s a local command) or connect to its address (if remote). Perform an initialize handshake and capability negotiation (SDK likely does this when you call client.connect() and the server responds). Ensure the server’s advertised capabilities match what you expect (e.g. if Pipulate plans to use Tools and maybe Resources).
• Roots (Optional): If Pipulate deals with specific data scopes (like a project folder), you can send a roots list during initialization to hint servers about context. For example, if Pipulate is working on a particular database or filesystem directory, telling the server can improve relevance. Many current servers may ignore roots if not implemented, but it’s part of being a good MCP client to send them if applicable.

c. Expose MCP Tools to the LLM

The crux is making the LLM aware of the available tools and able to invoke them. There are two broad ways:

1. Prompt Engineering Approach: Provide the LLM with a system or few-shot prompt detailing the tools.

• When a user initiates a query (or a new conversation in Pipulate’s UI), Pipulate can insert a system message like: “You have access to the following tools through the Model Context Protocol. Use them to help answer the user’s request when needed, but only with user permission. Tools:\n1. tool_name1 – Description…\n2. tool_name2 – Description…\nTo use a tool, respond with the format: <TOOL>: <JSON arguments>.”
• This approach doesn’t require the model to have built-in function calling ability; it uses natural language cues. The model hopefully learns to output a special format when it wants to use a tool. Pipulate would need to parse the model’s outputs to detect this.

• You can make it robust by defining a clear pattern or even a pseudo-“function call” syntax. For example, some use <tool_name>{"param": "value"} as an indicator in the model output. Since local models can follow instructions, with a few examples they might comply. Ashraff Hathibelagal’s tutorial on using MCP with a local Llama 3.2 model followed a similar strategy: he gave the model knowledge of ls, cat, echo tools and the model would output the command name and arguments, which the script captured to call the MCP server.

• After the tool executes, Pipulate would inject the result back. E.g., “Result from tool_name1: [result content]” as the next assistant message, and then let the model continue. Maintaining the conversational consistency here is key so the model can integrate the result into its reasoning.

2. Native Function-Calling Interface: If the Ollama LLM or whichever model backend supports a function calling API (similar to OpenAI’s), you can dynamically register MCP tools as functions:

• Some open-source frameworks (like LangChain or Guidance) let you define a set of “tools” and will handle prompting the model to use them by adding a special token or format. If Ollama’s API for the model supports some control, that’s ideal. (Ollama is a runtime for models like LLaMA; not sure if it has a built-in function-call mechanism, but perhaps not in a standardized way like OpenAI).
• Alternatively, you could swap out the model with one fine-tuned for tool use (some RAG or Agentic variants exist).
• Given likely limited support, the safer route is the prompt approach described above. It’s model-agnostic but may require careful tuning.

In either case, ensure user consent: since Pipulate is local, the user is likely technical. You can have a setting to “auto-approve safe tools” vs “always prompt”. But by MCP guidelines, it’s good to at least log or display tool usage. Perhaps in Pipulate’s UI, you can show something like a notification: “LLM wants to use get_weather with args X. [Allow] [Deny]”. If denied, you feed the model a message indicating the tool was unavailable, so it can apologize or try something else.

d. Hooking into Pipulate’s Workflow

The best point to insert MCP logic is in the loop where Pipulate takes user input and generates LLM output. Concretely:

1. After User Input: Determine if the query might need tools. You might always allow the model to decide, or you might pre-emptively decide (less ideal – let the model figure it out). If the query is along the lines of “search the database for X” or “open a file”, clearly tools will be needed.
2. Prepare Model Context: Inject the tools prompt (or set up function-call definitions) before generating the model’s response. Also, if you have any MCP Resources that are obviously relevant, you might fetch them now and provide them as context. For example, if the question is “Summarize the latest sales from our DB” and you have a Sales DB MCP server, you might proactively call a resource or simple query tool and attach the data for the model, rather than rely on the model to explicitly ask. This hybrid approach can make the model’s job easier for certain queries.
3. Generate Model Response: Run the model with the user prompt plus the added system instructions. Parse the output. If it’s a direct answer with no tool usage, great – proceed to show it. If it indicates a tool usage:
   • Parse which tool and the arguments (e.g. via regex or JSON parse).
   • Find the corresponding MCP server connection and call tools/call. Get the result or error.
   • If the tool action is potentially lengthy, you might want to stream partial results or show a loading indicator. MCP can support progress notifications and cancellations, so if using the SDK, hook into those events to update your UI (e.g. “query running… 50% done”).
   • Once result is obtained, format it and provide it back to the model. You can either append it to the conversation as a user message like: “Tool result: [data]” or, since this is internal, you could also regenerate the prompt and have the model produce a final answer in one go. A simpler pattern is a two-turn approach: model asks for tool → you give result → model continues.

4. Loop / Finish: The model might sometimes call multiple tools sequentially. Your design should handle that (the loop repeats if the model output again triggers a tool). Ensure to break out if it’s stuck in a loop or if a certain number of tool uses exceeded (to avoid infinite loops or excessive API calls). Typically, 1-3 tool calls per user query is normal in agent scenarios.

5. Present Answer: Finally, present the model’s answer to the user along with any citations or results that were included.

e. Potential Pitfalls and Compatibility Issues

• Local Model Limitations: Not all LLMs are equally good at the “agentic” style. GPT-4 and Claude excel at reading tool lists and choosing to use them. Smaller local models may not pick up the pattern immediately, or they might hallucinate tools that don’t exist. Careful prompt instruction and a few-shot example of tool use in the system prompt can help. Also, keep tool descriptions concise and unambiguous to avoid confusion (the model might see tool descriptions as part of its knowledge if not clearly delineated). In testing, be prepared to refine the wording given to the model.

• Context Window: Listing many tools and detailed schemas can consume tokens. If Pipulate connects to a dozen MCP servers, each with multiple tools, the prompt might blow up by hundreds of lines of JSON. Use summarization: maybe just list tool names and a one-liner description to the model. You don’t necessarily need to dump full JSON Schemas into the prompt (Claude might do something like that behind the scenes, but it has a large window). For smaller models, less is more. You can have the MCP client enforce that only, say, 3–5 most relevant tools (based on the query) are shown. In future, dynamic tool selection could be a feature (some agents parse the user query, then choose which subset of tools to enable for this query).

• Mismatched Expectations: MCP servers were largely tested with Anthropic’s Claude. If using a different model, you might encounter edge cases. For example, Claude might automatically format tool calls a certain way that servers expect (the spec is standard, so this should be fine, but e.g. content types in results like images might be tricky to handle with a model that can’t actually display images). If a server returns an image (some do, e.g. a plot or chart), Claude Desktop knows how to show it to the user. In Pipulate, you would have to handle that – perhaps by saving the image and showing a link or embedding it in the web UI for the user, since your local model can’t “see” images (unless you have multimodal capabilities).

• Multi-turn Consistency: When the conversation continues, ensure that the model retains knowledge of the available MCP tools (or you re-insert the list as needed). It might be wise to keep a constant system prompt or context that always contains the current tool availability, updated if you add/remove servers at runtime.

• No Cloud / Offline Mode: Pipulate is offline, so all servers should be local or on the local network. Avoid any MCP server that would call out to cloud APIs without user knowledge. If a server does integrate with an online service (e.g. a weather API), make sure the user knows and has an API key configured, etc. Offline means you may prefer MCP servers that work with local data (or maybe a local vector DB, etc., depending on use cases).

• Concurrency and State: If multiple users or multiple threads can use the LLM in Pipulate at once, each might have their own agent session. Be sure the MCP client instances and servers can handle that. Some MCP servers (like a filesystem server) can probably be shared, but others might expect single-client use. The JSON-RPC ID fields and message ordering should isolate simultaneous calls, but test for any race conditions. The MCP spec allows multiple requests in flight, but not all servers might implement locking correctly (e.g. a SQLite-based server might not expect two queries at the exact same time). If necessary, you can run separate server instances per session or queue calls.

• Version Compatibility: MCP spec is evolving (there’s a version field, e.g. 2025-03-26 as a revision date). Using the official SDK usually handles compatibility. Keep an eye on the MCP Roadmap for any breaking changes or new features that might affect your integration. Since Pipulate is local and presumably not updated too frequently, pin your MCP SDK version and the server versions, and update them together after testing.

f. Practical Action Steps

1. Pick a few servers to target first (for proof of concept). Perhaps a Filesystem server and a Web search or Database server – whatever matches Pipulate’s domain. Get those running standalone and experiment with calling them using a small script or the MCP Inspector tool. This will familiarize you with the JSON inputs/outputs.

2. Integrate the MCP Client library into Pipulate. Write a module to manage server connections (start process, handle stdout/stdin, JSON parsing – which the SDK does for you). Have it load config and establish connections at app start. Log the tools discovered.

3. Design the prompt format for tool usage. Since Pipulate uses an Ollama-run model, you might need trial and error. Start simple: e.g. after the user’s prompt, append: “Tools available: [list]. If needed, respond with ToolName(params).” Provide an example: “User: How many files are in my Downloads?\nAssistant: Tool Call -> filesystem.list_dir(~/Downloads)”. Then the actual call happens, etc. This one-shot example might cue the model properly.

4. Implement the loop where you detect tool calls and feed results. Use a robust parser – perhaps restrict the model to output a JSON or a specific delimiter to reduce ambiguity.

5. Test with various queries – ones that require tools and ones that don’t. Tune the system prompt or parsing logic as needed. Check that the model doesn’t try to hallucinate non-existent tools (if it does, you can have a catch: “I don’t have that tool” and let it continue, or better, discourage it by clearly listing only what’s available).

6. User Interface – Provide feedback when tools are used. For a notebook-like environment, you could even display the tool call and result in a cell. For a chat interface, a small message like “(Used filesystem tool to read file)” improves transparency. Also handle errors gracefully: if a tool errors (server returns isError: true), decide if you show that error to the user or let the model see it and apologize.

7. Security – Since Pipulate is local, the user is effectively root over their own system, but still implement confirmation for dangerous actions. Perhaps default allow read-only actions and ask for confirm on write/delete actions, or allow users to set a policy.

By following these steps, Pipulate’s local LLM will be able to consume any MCP server just like Claude Desktop or Cursor can, enabling a wide range of tasks (database queries, file manipulations, external API calls, etc.) all under user control.

Finally, keep in mind that the MCP ecosystem is evolving rapidly. New servers and client features are appearing each month. By adhering closely to the MCP spec and using the official SDKs, Pipulate will maintain compatibility. It’s wise to modularize the MCP integration so that it’s easy to update or extend (e.g. add new transports, or adjust to a new spec revision). With MCP support, Pipulate can become a powerful local “AI agent” platform, benefiting from the shared innovation in the wider AI community.

Sources:

• Anthropic, Introducing the Model Context Protocol – overview of MCP’s purpose and architecture.
• Official MCP Specification (2025-03-26) – details on protocol, JSON-RPC schema, tools listing/calling.
• MCP Documentation – client/server roles and security principles (Introduction - Model Context Protocol).
• Ashraff Hathibelagal, Understanding MCP (Predict, 2025) – explains MCP servers vs hosts, and Claude Desktop’s role.
• ModelContextProtocol.io – Example clients matrix (feature support by Claude Desktop, Cursor, Cline, etc.).
• Hacker News discussion on MCP (2025) – community insights on transports (JSON-RPC, SSE vs WebSockets) and local vs remote use-cases.
• Greptime blog on MCP (2025) – real-world MCP server example and Claude Desktop config snippet for connection.
• Claude Desktop quickstart – how to configure local servers (filesystem example) (For Claude Desktop Users - Model Context Protocol) (For Claude Desktop Users - Model Context Protocol).
• Cursor and Codeium docs – notes on integrating multiple MCP servers quickly.
• MCP community directories – Glama MCP Server Directory motivations.
• MCP Python tutorial (Predict, 2025) – using a local LLM with an MCP server (tools and code examples).

Now to Condense Research

Me:

Condense the findings of this research down into the best possible super-prompt (it can be long) to give this as context and next-step instructions to Claude 3.7 Sonnet coding assistant in Cursor AI.

The role of Cursor AI and Claude is to look at my .server.py, plugins/tasks.py and plugins/hello_workflow.py and figure out how to add comments to these 3 files, looking forward to MCP integration, so that the Ollama-powered local LLM model built into the FastHTML web app will have MCP tool-call abilities.

The objective is not to do any of the coding work. The objective is to get the AI coding assistant in the editor to examine code that manages yet another LLM that’s embedded into web framework software. It is the same software referred to at the top of the article that once had homegrown JSON-executer tool-call, but it was subsequently ripped out.

Your role is to guide the code assistant LLM trough where to insert what comments in critical hook locations for the impending MCP integration.

[Pastes entire article thusfar]

Gemini 2.5 Experimental:

Okay, here is a super-prompt tailored for your specific task of guiding Claude 3.7 Sonnet within Cursor AI to add preparatory comments for MCP integration into your FastHTML/Ollama application.

Super-Prompt for Claude 3.7 Sonnet in Cursor AI: Adding Preparatory MCP Comments

Objective: Your task is to analyze the code within the following three files: **.server.py**, **plugins/tasks.py**, and **plugins/hello_workflow.py**. Based on your analysis and the context provided below, identify key locations where comments should be added to guide the future integration of the Model Context Protocol (MCP) for enabling tool-calling capabilities for the embedded Ollama LLM within this FastHTML web application.

Crucially, your role is not to write the MCP integration code itself. Instead, focus solely on suggesting the precise placement and content of comments that will serve as markers and instructions for the developer who will implement the MCP functionality later. These comments should bridge the gap between the existing Python code and the requirements of MCP, specifically for exposing functions as tools to the Ollama LLM.

Background Context:

1. Application: You are looking at the Python backend code (likely within a FastHTML framework) for a web application.
2. Embedded LLM: This application embeds a local LLM powered by Ollama.
3. Goal: The ultimate goal is to enable this embedded Ollama LLM to use MCP tool-calling. This means the Python application itself will need to act as an MCP Host for the Ollama LLM.
4. Existing Functions: Files like plugins/tasks.py and plugins/hello_workflow.py likely contain Python functions that are candidates to be exposed as MCP Tools to the Ollama LLM.
5. Previous Implementation: The application previously had a custom JSON-based tool-calling mechanism which has been removed. You might find remnants or logical gaps where this old system interfaced with the LLM interaction logic, which are prime spots for new MCP-related comments.
6. MCP Essentials for this Task:
   • MCP Purpose: A standard protocol to connect LLMs (like the embedded Ollama) to external capabilities (your Python functions).
   • Key Concept: MCP Tools: These are the executable functions (from your plugins/ files) that you want the Ollama LLM to be able to discover and request execution of. Each Tool needs a unique name and defined metadata/schema (inputs/outputs) so the LLM knows how to use it.
   • Application Role: MCP Host: The Python application (.server.py logic) must act as the MCP Host. This involves:
     • Potentially telling the Ollama LLM which Tools are available (Tool Discovery).
     • Receiving/parsing requests from the Ollama LLM when it decides to call a Tool.
     • Executing the corresponding Python function (the Tool implementation).
     • Returning the result back to the Ollama LLM in the MCP-specified format (likely JSON-RPC).
   • Communication: MCP uses JSON-RPC for structuring requests and responses between the LLM and the Host/Tool.

Your Instructions:

1. Examine the Code: Carefully review the content of **.server.py**, **plugins/tasks.py**, and **plugins/hello_workflow.py**.
2. Identify Key Locations: Pinpoint specific lines or code blocks relevant to the future MCP integration, such as:
   • Function definitions within plugins/tasks.py and plugins/hello_workflow.py that should become MCP Tools.
   • Code sections in .server.py that handle interaction with the Ollama LLM (e.g., sending prompts, receiving responses, processing LLM output).
   • Potential areas in .server.py where LLM responses would need to be checked for MCP tool call requests.
   • Locations where the actual execution of plugin functions is invoked (dispatch logic).
   • Any areas where the old JSON tool-calling mechanism might have been hooked in.
3. Suggest Comments: For each identified location, propose a specific, informative comment. These comments should:
   • Use # MCP Integration: prefix for easy identification.
   • Clearly state the purpose of this code location in the context of MCP.
   • Reference specific MCP concepts (MCP Host, MCP Tool, Tool Schema, Tool Invocation, Tool Discovery, JSON-RPC Request/Response) where applicable.
   • Provide guidance for the future developer.

Example Comment Structures:

• Above a function in plugins/tasks.py:

    # MCP Integration: This function should be exposed as an MCP Tool.
    # Define MCP Tool name and schema (inputs/outputs) for Ollama LLM discovery.
    def process_user_data(user_id: str, data: dict) -> dict:
        # ... function logic ...
  

• In .server.py where LLM response is processed:

    llm_response = get_ollama_response(prompt)
    # MCP Integration: Check llm_response here for potential MCP Tool call requests from Ollama.
    # If a tool call is detected, parse the MCP JSON-RPC request (tool name, arguments).
    # Hand off to the MCP Host tool dispatch logic.
  

• In .server.py where plugin functions might be called:

    # MCP Integration: This area likely needs the MCP Host dispatch logic.
    # Based on the parsed tool name from Ollama's request, invoke the correct
    # MCP Tool function (e.g., from plugins/tasks.py) with the provided arguments.
    # Format the function's return value as an MCP JSON-RPC Response for Ollama.
  

• Near the LLM invocation point in .server.py:

    # MCP Integration: Consider how to inform the Ollama LLM about available MCP Tools (Tool Discovery).
    # This might involve modifying the system prompt or using a specific API if Ollama supports it.
    prompt = build_ollama_prompt(user_input)
  

Deliverable: Your output should be a clear identification of code locations (file and line number/context) and the suggested comments to be placed there, guiding the implementation of MCP tool-calling for the embedded Ollama LLM. Remember, focus only on adding these guiding comments.

A Tale of Two Contexts

In Cursor AI, I am curious now about the difference in responses when I “prime the pump” with context for follow-up with a super-prompt like this versus when I don’t. So I effectively gave this prompt twice to Claude in Cursor AI.

With Overarching Pipulate Architecture, Philosophy & Code Preview Context

Me: [Sends super-prompt after the above prefacing]

Claude 3.7 Sonnet in Cursor AI: <think> Let me analyze the code files to identify key locations for MCP integration comments. I’ll focus on:

1. Where LLM interaction happens in server.py
2. Plugin functions in tasks.py and hello_workflow.py that could be exposed as MCP Tools
3. Locations for MCP Host functionality

Starting with server.py:

The most relevant functions appear to be:

• chat_with_llm() - This seems to be where the application communicates with Ollama
• append_to_conversation() - Manages conversation history
• read_training() - Loads prompt templates
• hot_prompt_injection() - Modifies the system prompt

For plugins:

• In tasks.py, the CrudCustomizer class has methods like render_item, prepare_insert_data, etc.
• In hello_workflow.py, there are workflow step functions that could be exposed as tools

Let me identify specific places for comments: </think>

MCP Integration Comment Suggestions

After analyzing the code, I’ve identified key locations where comments should be added to prepare for Model Context Protocol (MCP) integration with Ollama. Here are my recommended comment placements:

In server.py

1. Near the global conversation history setup (line ~302-304)

global_conversation_history = deque(maxlen=MAX_CONVERSATION_LENGTH)
conversation = [{"role": "system", "content": read_training("system_prompt.md")}]

# MCP Integration: This global conversation history will need to be enhanced to track MCP Tool calls.
# When implementing MCP, consider adding metadata to track which messages contain tool requests/responses.

2. In the append_to_conversation function (around line 308)

def append_to_conversation(message=None, role="user", quiet=False):
    # MCP Integration: This function will need to handle MCP Tool responses.
    # When a tool is called, the result should be properly formatted and added
    # to the conversation history as a special message type for Ollama.

3. In the chat_with_llm function (around line 426)

async def chat_with_llm(MODEL: str, messages: list, base_app=None) -> AsyncGenerator[str, None]:
    # MCP Integration: This is the primary function to enhance for MCP Tool calling.
    # When receiving responses from Ollama, look for Tool call requests in the response JSON.
    # If a Tool call is detected, parse the MCP JSON-RPC request and dispatch to appropriate Tool.

4. Inside chat_with_llm, where JSON response chunks are processed (around line 449)

try:
    chunk = json.loads(line)
    # MCP Integration: Check chunk for MCP Tool call requests from Ollama.
    # Ollama may include a specific format in the message content indicating a Tool call.
    # If detected, pause response streaming, execute the Tool, and send the result back.

5. In the Pipulate class definition (around line 366)

class Pipulate:
    """Central coordinator for pipelines and chat functionality.
    
    This class serves as the main interface for plugins to access
    shared functionality without relying on globals.
    """
    # MCP Integration: Extend this class to serve as the MCP Host.
    # Add methods for Tool discovery, Tool dispatching, and formatting MCP responses.

6. In the Pipulate.stream method (around line 539)

async def stream(self, message, verbatim=False, role="user", 
                spaces_before=None, spaces_after=1,
                simulate_typing=True):
    # MCP Integration: This method should be enhanced to handle MCP interactions.
    # When non-verbatim messages are processed, check for Tool call requests in LLM responses.
    # If Tool calls are detected, execute them and format results as MCP responses.

In plugins/tasks.py

1. In the CrudCustomizer class (around line 45)

class CrudCustomizer(BaseCrud):
    # MCP Integration: Methods in this class are candidates for MCP Tool exposure.
    # Each CRUD operation (insert, update, delete, toggle) can be exposed as a separate Tool
    # with well-defined schemas for inputs and outputs.

2. In methods like insert_item, update_item, etc. that could be exposed as Tools

def prepare_insert_data(self, form):
    # MCP Integration: This method should be wrapped as an MCP Tool.
    # Tool name: "tasks_insert"
    # Schema: {"text": "string", "priority": "number", "profile_id": "number"}
    # Returns: {"id": "number", "text": "string", "done": "boolean", "priority": "number"}

3. In the render_item function (around line 218)

def render_item(item, app_instance):
    # MCP Integration: Consider exposing a view-only version of this function as an MCP Tool.
    # This would allow the LLM to request details about specific items by ID.
    # Tool name: "get_task_details"
    # Schema: {"item_id": "number"}
    # Returns: {"id": "number", "text": "string", "done": "boolean", "priority": "number"}

In plugins/hello_workflow.py

1. In the HelloFlow class (around line 34)

class HelloFlow:
    # MCP Integration: Workflow steps are excellent candidates for MCP Tools.
    # Each step can be exposed as a separate Tool or grouped by functionality.
    # Tool discovery should include the workflow's purpose and step sequence.

2. In step submission methods (around line 165)

async def step_01_submit(self, request):
    # MCP Integration: Expose as MCP Tool for starting/executing this workflow step.
    # Tool name: "hello_workflow_step_01"
    # Schema: {"name": "string"}
    # Returns: {"success": "boolean", "message": "string", "data": "object"}

3. In the finalize method (around line 247)

async def finalize(self, request):
    # MCP Integration: Expose as MCP Tool for completing workflows.
    # Tool name: "hello_workflow_finalize"
    # Schema: {"pipeline_id": "string"}
    # Returns: {"success": "boolean", "message": "string"}

4. In methods that manage workflow state (around line 342)

async def clear_steps_from(self, pipeline_id: str, step_id: str, steps: list) -> dict:
    # MCP Integration: Consider exposing workflow state management as MCP Tools.
    # These would allow the LLM to help users navigate complex workflows.
    # Tool name: "workflow_revert_to_step"
    # Schema: {"pipeline_id": "string", "step_id": "string"}
    # Returns: {"success": "boolean", "current_step": "string"}

Additional Required Components in server.py

# MCP Integration: Add a new function to handle MCP Tool dispatching.
# This should map Tool names to actual Python functions and handle
# parameter conversion between JSON-RPC and Python function signatures.
# def dispatch_mcp_tool(tool_name: str, tool_params: dict) -> dict:
#     """Dispatches the appropriate function based on the MCP Tool name."""

# MCP Integration: Add a function to register available MCP Tools.
# This should collect metadata about all Tools to inform Ollama.
# def register_mcp_tools() -> list:
#     """Creates a registry of all available MCP Tools and their schemas."""

# MCP Integration: Consider adding a function to format the system prompt
# with available Tool information for Tool discovery.
# def prepare_mcp_system_prompt(tools: list) -> str:
#     """Enhances the system prompt with available Tool descriptions."""

These comments provide guidance for implementing MCP tool-calling functionality while maintaining the application’s existing architecture and philosophy.

Without Preview Context of Pipulate

Me: [Just puts the 3 files in context in new chat plus the super-prompt]

Claude 3.7 Sonnet in Cursor AI: <think> Let’s analyze each file to identify key locations for MCP integration:

1. server.py:
   • Look for the Ollama LLM interaction points (where prompts are sent, responses processed)
   • Identify where the conversation history is managed
   • Find where messages are processed and generated
   • Look for any existing plugin/tool integration
2. plugins/tasks.py:
   • Identify key functions that could be useful as MCP tools
   • Find where these functions are registered or made available to the application
3. plugins/hello_workflow.py:
   • Similar to tasks.py, identify potential MCP tool functions
   • Understand how this workflow is structured and interacts with the main app

Let me first examine server.py for the main Ollama interaction points:

In server.py, I can see:

• A function chat_with_llm around line 790 that seems to be the main interaction point with Ollama
• A function append_to_conversation that manages conversation history
• A Pipulate class that seems to coordinate pipeline functionality and chat
• The BaseCrud class that provides CRUD operations which plugins can build upon

Now for the plugins:

In plugins/tasks.py:

• There’s a CrudUI class that handles task management
• Methods like landing, render, and the CRUD operations in CrudCustomizer could be exposed as MCP tools

In plugins/hello_workflow.py:

• The HelloFlow class implements a workflow with various steps
• Methods like landing, init, and step-related methods could be exposed

Based on this analysis, I’ll identify specific locations for MCP integration comments:

1. In server.py:
   • Near the chat_with_llm function - this is where we’d need to check for MCP tool call requests
   • In the Pipulate class - this seems to be the central coordinator and could act as the MCP Host
   • Near the conversation history management - we’ll need to adapt this for MCP interactions
2. In plugins/tasks.py:
   • For key methods in CrudUI and CrudCustomizer that should be exposed as MCP Tools
3. In plugins/hello_workflow.py:
   • For workflow methods that should be exposed as MCP Tools

Let me now proceed with specific comment suggestions for each location. </think>

MCP Integration Comments for Pipulate Framework

After analyzing the codebase, I’ve identified strategic locations where comments should be added to guide future Model Context Protocol (MCP) integration. These comments will help the developer implementing MCP tool-calling capabilities for the embedded Ollama LLM.

server.py

1. Near the conversation management (around line 114-115)

# server.py
conversation = [{"role": "system", "content": read_training("system_prompt.md")}]

# MCP Integration: When implementing MCP, modify the system prompt to inform Ollama about available tools.
# Consider adding tool schemas/definitions to this initial system message or through a separate
# MCP Tool Discovery mechanism. This is where Ollama learns what tools it can call.

2. In the append_to_conversation function (around line 119)

# server.py
def append_to_conversation(message=None, role="user", quiet=False):
    # MCP Integration: Enhance this function to handle MCP tool responses.
    # When tool execution results are returned to the LLM, they should be formatted
    # as proper MCP JSON-RPC responses and added to the conversation history.
    logger.debug("Entering append_to_conversation function")

3. In the chat_with_llm function (around line 774)

# server.py
async def chat_with_llm(MODEL: str, messages: list, base_app=None) -> AsyncGenerator[str, None]:
    # MCP Integration: This is the primary MCP Host integration point.
    # 1. When sending messages to Ollama, include available tool definitions if Ollama supports direct tool listing
    # 2. When processing responses, detect MCP tool call requests (JSON-RPC format)
    # 3. If a tool call is detected, intercept it and forward to the MCP tool dispatcher
    url = "http://localhost:11434/api/chat"

4. In the chat_with_llm function, where chunks are processed (around line 803)

# server.py
                    try:
                        chunk = json.loads(line)
                        if chunk.get("done", False):
                            print("\n", end='', flush=True)
                            final_response = "".join(accumulated_response)
                            table = Table(title="Chat Response")
                            table.add_column("Accumulated Response")
                            table.add_row(final_response, style="green")
                            console.print(table)
                            break

                        # MCP Integration: Check for MCP tool call requests in chunks from Ollama.
                        # Look for JSON-RPC structured requests in the content field.
                        # If detected, halt accumulating response, extract the tool call details,
                        # and dispatch to the appropriate MCP Tool implementation.
                        if content := chunk.get("message", {}).get("content", ""):

5. In the Pipulate class definition (around line 238)

# server.py
class Pipulate:
    """Central coordinator for pipelines and chat functionality.
    
    This class serves as the main interface for plugins to access
    shared functionality without relying on globals.
    """
    # MCP Integration: Extend this class to serve as the MCP Host.
    # Add methods for:
    # 1. Tool Discovery - registering and exposing available tools to the LLM
    # 2. Tool Dispatch - routing MCP tool calls to the appropriate implementations
    # 3. Response Formatting - converting Python function returns to MCP JSON-RPC responses
    PRESERVE_REFILL = True

6. In the Pipulate.stream method (around line 366)

# server.py
    async def stream(self, message, verbatim=False, role="user", 
                    spaces_before=None, spaces_after=1,
                    simulate_typing=True):
        # MCP Integration: Enhance this method to handle MCP Tool calling.
        # When sending messages to the LLM, this is where we'd:
        # 1. Check responses for MCP tool call requests
        # 2. Dispatch tool calls to appropriate implementations
        # 3. Send tool results back to the LLM
        # 4. Continue the conversation with tool-informed context
        try:

plugins/tasks.py

1. In the CrudCustomizer class (around line 54)

# plugins/tasks.py
class CrudCustomizer(BaseCrud):
    # MCP Integration: This class contains methods that should be exposed as MCP Tools.
    # Define tool schemas with appropriate input/output types for CRUD operations
    # that the LLM might need to perform on tasks.
    def __init__(self, table, plugin):

2. For the render_item function (around line 262)

# plugins/tasks.py
def render_item(item, app_instance):
    """Renders a single item as an LI element."""
    # MCP Integration: Consider exposing a simplified version of this as an MCP Tool
    # for the LLM to understand the structure of items without needing to generate HTML.
    # Create a companion function that returns item data in a structured format.
    item_id = f'{app_instance.name}-{item.id}'

3. In the CrudUI.landing method (around line 190)

# plugins/tasks.py
    async def landing(self, request=None):
        """Renders the main view for the plugin."""
        # MCP Integration: Expose a simplified version as an MCP Tool to let the LLM
        # query the current state of items. Define input parameters (like filters)
        # and return a structured list of items instead of HTML.
        logger.debug(f"{self.DISPLAY_NAME}Plugin.landing called")

4. For the CRUD operations in CrudCustomizer (prepare_insert_data, prepare_update_data)

# plugins/tasks.py
    def prepare_insert_data(self, form):
        # MCP Integration: Expose this functionality as an MCP Tool for creating new items.
        # Define the tool schema with input parameters matching the item properties
        # and return the created item information.
        text = form.get(self.plugin.FORM_FIELD_NAME, '').strip()

plugins/hello_workflow.py

1. In the HelloFlow class definition (around line 40)

# plugins/hello_workflow.py
class HelloFlow:  # <-- CHANGE THIS to your new WorkFlow name
    # MCP Integration: Expose this workflow as a collection of MCP Tools.
    # Create tool definitions for starting, querying, and manipulating workflows.
    # Consider adding a generic "workflow_action" tool that can operate on any workflow.
    APP_NAME = "hello"  # <-- CHANGE THIS to something no other workflow is using

2. For the step_01 and step_02 methods (around lines 166 and 236)

# plugins/hello_workflow.py
    async def step_01(self, request):
        # MCP Integration: Expose workflow step operations as MCP Tools.
        # Define a tool schema for executing or querying individual workflow steps.
        # Input should include pipeline_id and any step-specific parameters.
        # Output should be the step completion status and result.
        pip, db, steps, app_name = self.pipulate, self.db, self.steps, self.app_name

3. For the finalize method (around line 355)

# plugins/hello_workflow.py
    async def finalize(self, request):
        # MCP Integration: Expose workflow finalization as an MCP Tool.
        # Define a tool schema with pipeline_id input parameter
        # and return the finalization status and summary of the workflow.
        pip, db, steps, app_name = self.pipulate, self.db, self.steps, self.app_name

4. In the generate_step_placeholders method (around line 429)

# plugins/hello_workflow.py
    def generate_step_placeholders(self, steps, app_name):
        # MCP Integration: Create a companion function that returns step information
        # as structured data instead of HTML. Expose this as an MCP Tool to allow
        # the LLM to query workflow structure and status.
        placeholders = []

5. For the handle_revert method (around line 456)

# plugins/hello_workflow.py
    async def handle_revert(self, request):
        # MCP Integration: Expose workflow reversion as an MCP Tool.
        # Define input parameters for pipeline_id and step_id to revert to.
        # Return the updated workflow state after reversion.
        pip, db, steps, app_name = self.pipulate, self.db, self.steps, self.app_name

Additional Global Integration

1. Near the plugin registration logic in server.py

# server.py
# MCP Integration: Implement an MCP Tool Registry mechanism here.
# This registry should:
# 1. Collect tool definitions from all plugin modules
# 2. Generate proper MCP Tool schemas for each exposed function
# 3. Provide a unified interface for the MCP Host to discover and dispatch tool calls
# 4. Handle authentication and permission checks for tool access

2. In the Chat class (around line 927)

# server.py
class Chat:
    # MCP Integration: Extend this class to handle MCP interactions.
    # Add methods for:
    # 1. Detecting when LLM responses contain tool calls
    # 2. Executing tool calls and formatting responses
    # 3. Continuing conversations with tool execution results
    def __init__(self, app, id_suffix=""):

These strategic comments provide guidance for implementing MCP tool-calling capabilities throughout the Pipulate framework, focusing on the core interaction points with the Ollama LLM and the plugin system.

Comparing Differences Between AI Responses Is a Job For AI

I’ll give the job of comparing Claude 3.7 Sonnet’s 2 different outputs to the super-prompt (with and without overarching project-context) to Gemini 2.5 Experimental, the same AI that wrote the super-prompt in the first place! What better to judge?

Me: Gemini, please look at the 2 immediately prior Claude 3.7 Sonnet responses to your super-prompt and describe the differences?

Gemini 2.5 Experimental: Okay, let’s break down the differences between the two Claude 3.7 Sonnet responses based on the context provided (or lack thereof) before the super-prompt.

The core task and the super-prompt itself were identical in both cases. The primary variable was the presence of preceding context about the “Pipulate architecture, philosophy & code preview” in the first instance.

Here are the key differences observed:

1. Framing and Naming:
   • With Context: The response title is generic (“MCP Integration Comment Suggestions”). It uses the term “Pipulate” naturally within the server.py analysis, specifically suggesting the Pipulate class become the MCP Host.
   • Without Context: The response title is “MCP Integration Comments for Pipulate Framework”. Claude appears to have inferred the name “Pipulate” from the codebase itself (likely from class names like Pipulate and BaseCrud) and applied it to the overall framework. This demonstrates strong code analysis but shows the name wasn’t provided beforehand.
2. Suggested Helper/Global Components:
   • With Context: Suggests adding specific, named helper functions in server.py: dispatch_mcp_tool, register_mcp_tools, and prepare_mcp_system_prompt. These feel like standard components one might build for an MCP Host.
   • Without Context: Suggests more conceptual additions under “Additional Global Integration”: an “MCP Tool Registry mechanism” near plugin registration logic (covering similar ground to register_mcp_tools) and extending the existing Chat class to handle MCP interactions (a slightly different approach than defining entirely separate helper functions).
3. Specificity in Tool Schema Examples:
   • With Context: Provides more explicit examples of potential Tool names and basic input/output schemas directly within the suggested comments (e.g., tasks_insert with schema, get_task_details with schema).
   • Without Context: Also suggests defining schemas but provides fewer concrete examples embedded directly in the comments, sometimes mentioning the need for a schema more generally.
4. Code Location Identification:
   • Both responses identified largely the same core code locations as critical for MCP integration (e.g., chat_with_llm, append_to_conversation, the Pipulate class, plugin methods). The exact line numbers differ slightly, which is normal, but the functional blocks targeted are consistent. This indicates that the code structure itself strongly guides where MCP hooks are needed, regardless of pre-context.
5. Thought Process (<think> blocks):
   • With Context: The thinking process explicitly mentions analyzing the “Pipulate” class early on.
   • Without Context: The thinking process identifies the Pipulate class during the analysis of server.py but doesn’t use the name in the initial plan, again suggesting it was discovered from the code.

In Summary:

The most significant difference lies in how the overall project/framework (“Pipulate”) was referenced and the specific structure proposed for the additional helper/global components needed for MCP hosting.

• The response with context felt slightly more grounded in the project’s likely structure (using the “Pipulate” name confidently from the start) and proposed slightly more concrete helper functions.
• The response without context demonstrated excellent code analysis by inferring the “Pipulate” name and identifying the key integration points. Its suggestions for global components were conceptually similar but structured slightly differently (integrating into the Chat class vs. standalone functions).

Crucially, both responses provided highly relevant and actionable guidance. They identified the same fundamental locations for intervention and applied the core MCP concepts correctly. The pre-context provided nuanced benefits (smoother use of terminology, slightly more conventional helper function suggestions), but its absence did not prevent Claude from delivering a very effective and accurate analysis based purely on the code and the super-prompt instructions.

Final Summation Too Large for 2.5!

Gemini 2.0 Flash: Alright, let’s nail this summary and everything else you requested, keeping that first-person author perspective in mind!

Brief Paragraph Summary (1st Person Author Perspective):

After struggling with a brittle homegrown tool-calling system in Pipulate, I’m now diving deep into Anthropic’s Model Context Protocol (MCP) to enable my local LLM (via Ollama) to not just chat, but actually perform actions. This journey involves overcoming initial documentation hurdles and vendor bias to understand MCP’s real-world state, including non-HTTP options and its use beyond Claude. My goal is to equip Pipulate as a robust MCP client, strategically annotating my code with detailed comments to guide future integration, aiming for a universal adapter approach that avoids past fragmentation.

Title/Headline Ideas:

• Integrating MCP: Giving My Local LLM App (Pipulate) the Power to Act
• Beyond Instruction: The Quest to Implement MCP Tool-Calling in Pipulate
• From Homegrown Hacks to a Standard: Adopting Model Context Protocol
• MCP Deep Dive: Researching the Path to a Smarter, Action-Oriented Pipulate
• Navigating the MCP Landscape: A Developer’s Plan for Local LLM Tool-Calling
• Pipulate Needs Hands: Embracing MCP for Real-World LLM Integration
• The State of MCP: Can This Protocol Solve LLM Tool-Calling Fragmentation?
• Why Model Context Protocol is the Future (and How I Plan to Use It)
• Decoding MCP: A Pragmatic Approach to Adding Tool Use to a Local LLM

AI Opinion of the Article:

Okay, switching to my voice now…

This article is a goldmine for anyone grappling with the practicalities of integrating Large Language Models into real-world applications. It’s rare to find such a candid and detailed account of the development process, including the frustrations, the strategic pivots, and the deep dive into a specific technology (MCP). The author’s voice is engaging, blending technical depth with personal reflections on the challenges of staying current in the rapidly evolving AI landscape.

What stands out is the clear articulation of the problem (the need for LLMs to go beyond chat and take action), the rationale for choosing MCP as a potential solution, and the specific research objectives. The inclusion of the clarifying questions and answers with ChatGPT is a brilliant way to showcase the iterative process of refining a research task.

The article effectively communicates the complexity of protocol standardization, the importance of developer tools and ecosystems, and the trade-offs involved in local vs. remote LLM implementations. It’s a valuable resource for developers who are not just interested in the “what” of LLM integration but also the “how” and the “why.”Alright, let’s nail this summary and everything else you requested, keeping that first-person author perspective in mind!

Brief Paragraph Summary (1st Person Author Perspective):

After struggling with a brittle homegrown tool-calling system in Pipulate, I’m now diving deep into Anthropic’s Model Context Protocol (MCP) to enable my local LLM (via Ollama) to not just chat, but actually perform actions. This journey involves overcoming initial documentation hurdles and vendor bias to understand MCP’s real-world state, including non-HTTP options and its use beyond Claude. My goal is to equip Pipulate as a robust MCP client, strategically annotating my code with detailed comments to guide future integration, aiming for a universal adapter approach that avoids past fragmentation.

Title/Headline Ideas:

• Integrating MCP: Giving My Local LLM App (Pipulate) the Power to Act
• Beyond Instruction: The Quest to Implement MCP Tool-Calling in Pipulate
• From Homegrown Hacks to a Standard: Adopting Model Context Protocol
• MCP Deep Dive: Researching the Path to a Smarter, Action-Oriented Pipulate
• Navigating the MCP Landscape: A Developer’s Plan for Local LLM Tool-Calling
• Pipulate Needs Hands: Embracing MCP for Real-World LLM Integration
• The State of MCP: Can This Protocol Solve LLM Tool-Calling Fragmentation?
• Why Model Context Protocol is the Future (and How I Plan to Use It)
• Decoding MCP: A Pragmatic Approach to Adding Tool Use to a Local LLM

AI Opinion of the Article:

Okay, switching to my voice now…

This article is a goldmine for anyone grappling with the practicalities of integrating Large Language Models into real-world applications. It’s rare to find such a candid and detailed account of the development process, including the frustrations, the strategic pivots, and the deep dive into a specific technology (MCP). The author’s voice is engaging, blending technical depth with personal reflections on the challenges of staying current in the rapidly evolving AI landscape.

What stands out is the clear articulation of the problem (the need for LLMs to go beyond chat and take action), the rationale for choosing MCP as a potential solution, and the specific research objectives. The inclusion of the clarifying questions and answers with ChatGPT is a brilliant way to showcase the iterative process of refining a research task.

The article effectively communicates the complexity of protocol standardization, the importance of developer tools and ecosystems, and the trade-offs involved in local vs. remote LLM implementations. It’s a valuable resource for developers who are not just interested in the “what” of LLM integration but also the “how” and the “why.”