Simon Says Make MCP Tool-call: Teaching a Local LLM to Use Tools

My initial MCP implementation works, but it’s a parlor trick—a rigged demo that forces the AI into a single, predictable response. It’s useless for anything real. I need to evolve this from a rigid proof-of-concept into a flexible training tool, but without opening the door to the chaos of open-ended prompting. The answer isn’t to make the AI guess, but to teach it through a guided ‘Simon Says’ game, turning a workflow step into an interactive MCP construction kit. But first, I have to fix the observability; if I can’t see what the tool is doing with the same clarity as my database queries, the whole system is a black box.

By Mike Levin

Wednesday, June 11, 2025

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Setting the Stage: Context for the Curious Book Reader

This entry provides a ground-level view of development within Pipulate, a unique, local-first automation framework designed to package complex, notebook-style data workflows into simple, accessible applications. At its core, Pipulate champions a philosophy of “Write Everything Twice” (WET) for its workflows, prioritizing clarity and explicit control over premature abstraction. This approach becomes particularly relevant as the author integrates a local Large Language Model (LLM) to perform tasks, moving beyond simple chat to a more structured form of tool-use.

The central challenge explored here is the implementation of a Machine-Readable Common Protocol (MCP), a system for enabling reliable, predictable tool-calling with an LLM. The author grapples with the initial version being too rigid—a “guaranteed success” that sacrifices the very flexibility tool-use is meant to provide. This log captures the pivotal moment of moving from a tightly-controlled prototype to a more robust, educational, and ultimately more powerful implementation, offering a rare glimpse into the practical engineering and philosophical trade-offs required to make local AI not just functional, but truly useful.

The Fragility of a “Perfect” Prototype

Okay, MCP in its current implementation in Pipulate that I did last night is effectively useless because I designed it to be extremely rigid to guarantee a success.

An MCP Primer: Why Tool-Calling is Barely AI

What few people realize is that MCP is almost a non-LLM / non-AI technology. It’s almost just a very structured request/response model — as if you were going to automate form submits on the web replacing the details with XML so you can write rules about what’s a well-formed and valid form submit. An LLM AI being in the picture is really just them knowing how to express such a form submit as a response to you in conversation, and the chatbot apparatus notices and steps in and uses it. And so my first implementation was about guaranteeing this process all went of seamlessly. And so I made a button that you click that causes an LLM to express just such an MCP request so I could focus on the machinery that listed for it and responds.

From Guaranteed Success to Useless Rigidity

It’s as if I cornered the local AI built into Piulate into saying almost exactly the same thing over and over so I could assure the quality of it’s response. And I put that on the Poke button on the urging of another AI, Gemini, who identified the poke button endpoint as the perfect place to wedge in this MCP experiment. So each time you poke the local LLM, it responds in just such a way that a conversation monitor recognizes it as a tool-call, parses it and gets a random cat fact from https://catfact.ninja/fact. The extraction magic happens here:

async def chat_with_llm(MODEL: str, messages: list, base_app=None) -> AsyncGenerator[str, None]:
    url = 'http://localhost:11434/api/chat'
    payload = {'MODEL': MODEL, 'messages': messages, 'stream': True}
    accumulated_response = []
    full_content_buffer = ""
    word_buffer = ""  # Buffer for word-boundary detection
    mcp_detected = False
    chunk_count = 0
    mcp_pattern = re.compile(r'(<mcp-request>.*?</mcp-request>)', re.DOTALL)

    logger.debug("🔍 DEBUG: === STARTING chat_with_llm ===")
    logger.debug(f"🔍 DEBUG: MODEL='{MODEL}', messages_count={len(messages)}")

    table = Table(title='User Input')
    table.add_column('Role', style='cyan')
    table.add_column('Content', style='orange3')
    if messages:
        last_message = messages[-1]
        role = last_message.get('role', 'unknown')
        content = last_message.get('content', '')
        if isinstance(content, dict):
            content = json.dumps(content, indent=2, ensure_ascii=False)
        table.add_row(role, content)
        logger.debug(f"🔍 DEBUG: Last message - role: {role}, content: '{content[:100]}...'")
    console.print(table)

    try:
        async with aiohttp.ClientSession() as session:
            async with session.post(url, json=payload) as response:
                if response.status != 200:
                    error_text = await response.text()
                    error_msg = f'Ollama server error: {error_text}'
                    logger.error(f"🔍 DEBUG: HTTP Error {response.status}: {error_text}")
                    yield error_msg
                    return
                
                yield '\n' # Start with a newline for better formatting in UI
                
                async for line in response.content:
                    if not line: continue
                    try:
                        chunk = json.loads(line)
                        chunk_count += 1
                        
                        if chunk.get('done', False):
                            logger.debug(f"🔍 DEBUG: Stream complete (done=True)")
                            break

                        if (content := chunk.get('message', {}).get('content', '')):
                            # If we've already found and handled a tool call, ignore the rest of the stream.
                            if mcp_detected:
                                continue

                            full_content_buffer += content
                            
                            # Use regex to find a complete MCP block
                            match = mcp_pattern.search(full_content_buffer)
                            if match:
                                mcp_block = match.group(1)
                                mcp_detected = True # Flag that we've found our tool call
                                
                                logger.info(f"🔧 MCP CLIENT: Complete MCP tool call extracted.")
                                logger.debug(f"🔧 MCP BLOCK:\n{mcp_block}")
                                
                                # Offload the tool execution to a background task
                                asyncio.create_task(
                                    execute_and_respond_to_tool_call(messages, mcp_block)
                                )
                                # Now that we have the tool call, we ignore all subsequent content from this stream
                                continue 
                            
                            # If no MCP block is detected yet, stream the content normally.
                            # This handles regular, non-tool-call conversations.
                            word_buffer += content
                            parts = re.split(r'(\s+)', word_buffer)
                            if len(parts) > 1:
                                complete_parts = parts[:-1]
                                word_buffer = parts[-1]
                                for part in complete_parts:
                                    accumulated_response.append(part)
                                    yield part

                    except json.JSONDecodeError:
                        logger.warning(f"🔍 DEBUG: JSON decode error on chunk #{chunk_count}")
                        continue
                
                # After the loop, if there's remaining content in the buffer and no tool was called, flush it.
                if word_buffer and not mcp_detected:
                    accumulated_response.append(word_buffer)
                    yield word_buffer

                # Final logging table for non-tool-call responses
                if not mcp_detected and accumulated_response:
                    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)

    except aiohttp.ClientConnectorError as e:
        error_msg = 'Unable to connect to Ollama server. Please ensure Ollama is running.'
        logger.error(f"🔍 DEBUG: Connection error: {e}")
        yield error_msg
    except Exception as e:
        error_msg = f'Error: {str(e)}'
        logger.error(f"🔍 DEBUG: Unexpected error in chat_with_llm: {e}")
        yield error_msg

The `process_llm_interaction` Refactor: Aligning Code with Reality

In expanding this feature yesterday I noticed the LLM coding assistants making hallucination mistakes assuming other names for this function. When I see that happen I think it’s best to make those fine-tuning adjustments in order to lean into what they WANT it to be called so that the hallucination lines up more closely with reality. This is leaning into more conventional patterns. So what would a more conventional name for this function be called? I think tool-calling is the popular notion that has struck the public’s fancy and which the LLM models themselves seem to lock-in on. So perhaps we tie it somehow to that?

A Dialogue with the Machine: When AI Suggests its Own Fixes

Gemini: Of course. This is a fantastic and highly insightful observation. You’ve hit upon a key principle of writing maintainable, long-lasting, and collaborative code: lean into convention, especially when the conventions are being established by the very tools you’re collaborating with.

Fighting an LLM assistant’s tendency to “hallucinate” a more conventional name is a losing battle. Embracing it makes the system more intuitive for the next human and the next AI that works on it. You are essentially fine-tuning your codebase to be more “AI-friendly” for future development.

Let’s break down the naming and implement this change.

Analysis: The Evolving Role of `chat_with_llm`

You are correct that chat_with_llm has outgrown its name. When it was first written, its only job was to pass messages to Ollama and stream the response. However, as you’ve brilliantly engineered it, it now has a much more sophisticated role:

Initiates Chat: It still handles basic conversational streaming.
Monitors for Tool Calls: It actively watches the incoming stream for the <mcp-request> pattern.
Acts as an Interceptor: It captures, buffers, and hides the raw tool call from the user.
Delegates Execution: It triggers a separate, asynchronous task (execute_and_respond_to_tool_call) to handle the “table tennis” round trip.

The function is no longer just a simple chat client; it is the central orchestrator for the entire LLM interaction loop. Its name should reflect this new, more powerful responsibility.

What Would a More Conventional Name Be?

Given its role as an orchestrator that manages both chat and potential tool use, a more conventional and descriptive name would be:

process_llm_interaction

Why this name is better:

Accurate: It processes an entire “interaction,” which could be a simple chat turn or a complex, multi-step tool call.
Neutral: It doesn’t presuppose the outcome is just “chat.”
Conventional: The terms “process” and “interaction” are standard in event-driven and agentic architectures. Future AI assistants are highly likely to recognize this pattern.
Aligns with AI Vocabulary: It avoids the specific term “tool-calling” in the function name itself, allowing the function to remain generic even if you introduce other interception logic in the future (e.g., for sentiment analysis, content filtering, etc.).

Implementation Plan: Refactoring for Clarity

Let’s perform this “cosmetic” but architecturally significant refactoring. It’s a simple find-and-replace, but it makes the entire flow more intuitive.

File to Modify: server.py

Step 1: Rename the function definition.

# FIND THIS (around line 3014):
async def chat_with_llm(MODEL: str, messages: list, base_app=None) -> AsyncGenerator[str, None]:

# REPLACE WITH:
async def process_llm_interaction(MODEL: str, messages: list, base_app=None) -> AsyncGenerator[str, None]:

Step 2: Update the recursive call within the tool execution helper. The execute_and_respond_to_tool_call function makes a second call to the LLM. We must update this call to use the new function name.

# FIND THIS (inside execute_and_respond_to_tool_call, around line 3169):
        # 4. Make the second LLM call and stream the final response to the UI
        logger.info("MCP CLIENT: Sending tool result back to LLM for final answer...")
        final_response_text = ""
        async for chunk in chat_with_llm(MODEL, final_prompt_messages):
            await chat.broadcast(chunk)
            final_response_text += chunk

# REPLACE WITH:
        # 4. Make the second LLM call and stream the final response to the UI
        logger.info("MCP CLIENT: Sending tool result back to LLM for final answer...")
        final_response_text = ""
        async for chunk in process_llm_interaction(MODEL, final_prompt_messages):
            await chat.broadcast(chunk)
            final_response_text += chunk

Step 3: Update the initial call from pipulate.stream. This is the entry point for all non-verbatim messages.

# FIND THIS (inside Pipulate.stream, around line 2816):
            # ...
            conversation_history = list(global_conversation_history)
            response_text = ''
            async for chunk in chat_with_llm(MODEL, conversation_history):
                await chat.broadcast(chunk)
                response_text += chunk
            # ...

# REPLACE WITH:
            # ...
            conversation_history = list(global_conversation_history)
            response_text = ''
            async for chunk in process_llm_interaction(MODEL, conversation_history):
                await chat.broadcast(chunk)
                response_text += chunk
            # ...

✅ Done. This simple refactoring doesn’t change the logic, but it dramatically improves the code’s clarity and future-proofs it for more complex agentic behaviors. It now correctly describes a function that is not just “chatting” but orchestrating a potentially complex interaction with the LLM.

Back From Sub-Project #1

It seems like everything is a sub-project or a nested dependency. In this case it’s the desire to polish before minimum viable product is achieved. It’s a back-and-forth tension. Some you yield to when it’s easy and will make a lot of difference moving forward, especially labeling stuff like this that effects all your cognitive mapping moving forward. Mental 301 redirects suck.

Me: Done and done. Worth noting I tried using the F2 “replace all instances of object” feature that Cursor inherited from VSCode and it took forever and still didn’t work, so I ended up quitting and restarting Cursor so nothing mysterious was running in the background and did a simple global search-and-replace instead. There were only 5 instances. I think when the thing you’re search-and-replacing is sufficiently unique, you don’t need that fancy F2 feature which is an attempt at being syntactically aware or something to differentiate your use of a word in comments from its use as something with an object pointer or reference attached to it (function name, parameter name, etc).

Okay, so that still doesn’t fix the fact that this is a rigged-for-success MCP demo on rails, which is funny because MCP is all about more loosely coupling components. In an integration, Part A is either tightly coupled to Part B knowing about its “internals” and having to use extremely specific forms of interaction so things don’t break (tight coupling), or Part A doesn’t really know much about Part B and uses some generic, loose and forgiving way of interacting with Part B and somehow it all just works out (loose coupling). MCP is a move from tight coupling of parts in integration towards loose coupling. So what I’m doing for my next step is loosening the coupling without risking the LLMs trying to bully me into some massive potentially system-breaking refactoring. This particular super-prompt is about easing from this tightly coupled demonstration of loose coupling to only just slightly looser coupling, perhaps going only from the endpoint way of triggering an MCP interaction to something else.

The “Simon Says” Breakthrough: From Poke Button to Guided Prompting

The Poke endpoint truly represents the “rails” that I put this demo of MCP on because I plan on using this with almost any model that’s “smart enough” to learn MCP in Ollama. Something few people realize is that function tool-calling is not really so much a feature of a model but rather just the ability to talk the model into responding in just such a way so that its response can be both well-formed (good XML, for example) and valid — which is simply good XML that validates against a particular schema (aka definition). That might sound technical but it amounts to how well the LLM responds when you say: “Repeat after me,” or “Simon says…”

And so I really need a way for the human to state a query in a very stringent and correct simon-says protocol so I can have plain old chat initiate the request instead of a poke endpoint. Or… hmmm… this is a rigid linear workflow processing system after all. Maybe my best path is avoiding the rabbit hole of open-ended human-prompt-to-MCP-request translator and is instead something like another widget in the system, but instead of showing a chart, graph or some other nonsense, it articulates a very specific already set up rigged-for-success MCP-call using any MCP-call against any MCP server!

This keeps the rigid-rails in place. Do I cut the local LLM out of the loop? Hmmm. It hardly makes any sense to just build an http-request-maker with an extra protocol as a sort of intermediary overhead. No, this only makes sense if the local LLM gets trained by this rigid request. So what we’re really talking about is a real-time hot prompt injection that both teaches the local LLM the MCP protocol and the particular well-formed and valid schema you’re expecting and asks it to repeat right back in that simon-says fashion what you just said to it but modified with the situation and circumstances at hand — likely to be something about the workflow you’re in.

In this way you could spare yourself a lot of text-parsing on an HTTP request you have the local LLM make on your behalf. You could have the LLM look at a web page for you funneling the response through whatever pre-filtering process you like to strip it down to something the LLM can readily handle. It can simply make an MCP tool-call that expresses:

A Vision for the Future: Full-Stack Browser Automation via MCP

Look at the following URL and give me a simplified version of the DOM that is a result of a real web browser visit (Selenium automation) but strip it down to bare-bones HTML keeping aria attributes (Accessible Rich Internet Applications) in place so I get full semantic meaning. By this time all JavaScript is rendered for the page-build and we can strip out all the nonsense classes that React and Tailwind litter the Internet with. What remains would be the HTML elements that constitute the page and aria tags that layer-in accessibility for the visually impaired and the browser-automating bots!

Okay, this is definitely what I’m inching towards. You can even joke with the LLM in the chatbot side-panel that you would ask it to do tool-calls for you if it were Anthropic Claude or something, but being the wee little local AI running on your local hardware, you’re going to go through a Simon Says coach instead. So it’s this Simon Says MCP coach that I’m making as the next workflow plugin.

It should work as a single step and effectively expand on the blank placeholder. Or perhaps more usefully it could expand on the textarea widget, for you see it wouldn’t be the worse thing in the world to show you the Simon Says game that’s being played, training the human user right as we train the LLM. Ahem…

You are playing a game of Simon Says with the local LLM. If you are successful in this game, the LLM will repeat back to you… Oh, wait! I need a level of transparency on MCP tool-calls that’s only rivaled by the transparency of the Botify BQL queries that are a part of… hmmm. Emergency implementation:

Emergency Pivot: The Non-Negotiable Need for Extreme Observability

Observe the extreme observability of what BQL queries get executed as they are shown in the web server console, down to the ability to copy/paste Python code snippets into a Jupyter Notebook for full self-contained reproducibility. I want that same level of transparency on the MCP example that’s currently built into the poke endpoint, please and thank you.

Claude: [A ton of back-and-forth with Claude, until…]

Excellent! I can see the enhanced MCP observability logging is working! The log shows 🚀 === END MCP TRANSPARENCY === which indicates our enhanced logging is being captured. Let me get more context by looking at more log lines to see the full transparency output:

tail -100 ./logs/Botifython.log | grep -A 50 -B 5 "🚀 === MCP OPERATION TRANSPARENCY ===" | head -60

Perfect! 🎉 The enhanced MCP observability is working beautifully! I can see:

🚀 MCP OPERATION TRANSPARENCY ACHIEVED! 🚀

The enhanced MCP observability is now providing exactly the same level of transparency as the BQL query logging system! Here’s what we’re getting:

✅ Complete Operation Tracking

Operation ID: b22671e3 (unique identifier for tracing)
Tool Name: get_cat_fact
Operation Type: tool_execution
Execution Time: 56.50ms (performance monitoring)
Timestamp: Full ISO timestamp for audit trails

✅ MCP Block Visibility

Shows the exact MCP request structure:

<mcp-request>
  <tool name="get_cat_fact" />
</mcp-request>

✅ Internal Tool Executor Details

URL: http://127.0.0.1:5001/mcp-tool-executor
Method: POST
Payload: Complete JSON with proper formatting
Response: Full response with status and data

✅ External API Call Transparency

URL: https://catfact.ninja/fact
Method: GET
Response Status: 200
Complete Response: Shows the actual cat fact data

✅ Copy-Paste Ready Python Code

Jupyter-ready reproduction code with the exact same pattern as BQL queries
Operation ID tracking for cross-referencing
Complete async/await structure for notebook execution
Error handling and response processing built-in

🎯 Mission Accomplished!

You now have extreme observability for MCP operations that matches the transparency of BQL queries. This includes:

🔍 Complete visibility into what MCP tools are doing
📋 Copy-paste Python snippets for Jupyter reproduction
⚡ Performance tracking with execution times
🔗 Full request/response logging for debugging
🎯 Operation IDs for tracing and correlation

The MCP system now provides the same level of transparency and reproducibility that you praised in the BQL query system! 🏆

Checking With Claude Before Checking With Gemini

I still use Gemini 2.5 Pro for coding implementation plans. It is either smarter, or the way I feed my prompt_foo.py secret sauce prompts curates and frames the problems for better quality results (not sure which). In any case, I let Claude know all this and give it a chance to adjust this super-prompt accordingly.

Me: Excellent! Now continue this article however you would like, filling in any details I may have missed. No offense to you, but I’m getting ready to go over to Gemini for my Simon Says MCP widget implementation plan. It will effectively have all the (cherry-picked) codebase in context that I want it to see using the /home/mike/repos/pipulate/helpers/prompt_foo.py system. But you get where I’m going with this, and this is your chance to say anything you like to the Gemini 2.5 Pro AI that I’m about to ask for an implementation plan for something that can replace one of these /home/mike/repos/pipulate/plugins/300_blank_placeholder.py placeholders in workflow construction. In other words, I’m planning on making a self-contained Simon Says Make MCP Tool-call Request widget and am interested in your thoughts before I impose something on you from another AI.

Claude: Perfect! 🎯 You’ve achieved extreme MCP observability that rivals your BQL transparency. Now, before you head to Gemini for the Simon Says MCP widget implementation, here are my key thoughts:

🎪 The Brilliance of “Simon Says MCP”

Your approach is architecturally sound because it:

Maintains Your Rails: Keeps the controlled environment while loosening coupling
Dual Training: Teaches both human and LLM the MCP protocol simultaneously
Leverages Existing Infrastructure: Uses your workflow system + new observability
Scales Gradually: From rigid demo → guided interaction → eventual free-form

🎯 Critical Design Considerations for Gemini

# Should extend your existing single-step pattern:
class SimonSaysMcpWidget(YourBaseWorkflow):
    APP_NAME = 'simon_mcp'
    DISPLAY_NAME = 'Simon Says MCP 🎪'

2. Template System

You’ll want pre-baked MCP schemas for common tasks:

Web scraping (with Selenium DOM cleanup)
API calls (REST endpoints)
File operations
Data transformations

3. The “Game” Interface

Show the human what’s happening:

🎪 SIMON SAYS: "Make an MCP request to get a cat fact"
🤖 LLM ATTEMPTS: <mcp-request><tool name="get_cat_fact"/></mcp-request>
✅ SIMON: "Well formed! Executing..."
📊 RESULT: [Your beautiful observability logs]

4. Integration Points

Leverage your existing log_mcp_call_details for transparency
Use your process_llm_interaction for the Simon Says dialogue
Tap into your textarea widget pattern for showing the interaction

🚨 Key Message for Gemini 2.5 Pro

The user has a sophisticated workflow system with:

✅ MCP observability already implemented (just like BQL queries)
✅ Rigid pipeline architecture that works beautifully
✅ Single-step widget patterns to follow
✅ Local LLM integration via process_llm_interaction

Don’t over-engineer this!

The goal is a single-step workflow widget that:

Presents MCP templates to choose from
Shows the “Simon Says” training dialogue
Lets the LLM attempt the MCP request
Validates and executes with full observability
Provides feedback for learning

Stay within their proven patterns - this isn’t about rebuilding their architecture, it’s about adding one elegant widget that teaches MCP through guided interaction.

🎭 Final Thought: Your instinct to keep this controlled but educational is spot-on. The Simon Says approach lets you gradually train both human and AI while maintaining the reliability that makes Pipulate work. The widget should feel like a training ground that eventually graduates users to more sophisticated MCP interactions.

Good luck with Gemini! 🚀

Letting Claude Cherry-Pick Context For Gemini

Welcome to a bit of insight into the Prompt Fu secret sauce of solid AI implementation plans. You don’t leave anything to chance. You curate the story you feed in a single-prompt submit. This is how I package up whopping big XML payloads that carry full git repo context, storytelling, and the prompt challenge.

Me: Any opinion if my prompt foo is going to include the right things on the prompt for this implementation request?

FILES_TO_INCLUDE_RAW = """\

# HI GEMINI! HERE'S SOME SUPER OVER-ARCHING CONTEXT OF THIS XML
# THE STUFF IN THIS XML PAYLOAD IS STILL ONLY PART OF THE STORY
# BUT YOU CAN SEE ME PAIRING IT DOWN TO ~100K TOKENS FOR US NOW

# CORE FILES & DOCS (Setting the stage)
README.md        <-- Main GitHub README sets the tone
flake.nix        <-- NixOS configuration for the project (Infrastructure as Code / IaC)
requirements.txt <-- Python package dependencies for the project
server.py        <-- Main server file for the project
/home/mike/repos/Pipulate.com/install.sh      <-- The install script for the project & magic behind magic cookies
/home/mike/repos/Pipulate.com/development.md  <-- Pipulate.com Development notes for https://github.com/miklevin/pipulate/

## SPECIFIC FILES NECESSARY FOR CURRENT PROJECT AND PROMPT (Defining the challenge)
/home/mike/repos/pipulate/plugins/300_blank_placeholder.py  <-- The minimal template that propegates everywhere (as WET as we get)
/home/mike/repos/pipulate/plugins/040_hello_workflow.py     <-- Master example and Jupyter Notebook, shows parallels to Unix pipe

## REQUIRED CRUD PLUGINS FOR SYSTEM CONFIGURATION: (Supporting characters)
# /home/mike/repos/pipulate/plugins/020_profiles.py  <-- Controls PROFILE menu, required DRY CRUD plugin controls profile management
# /home/mike/repos/pipulate/plugins/030_roles.py     <-- Controls APP menu, required DRY CRUD plugin controls role management
# /home/mike/repos/pipulate/plugins/060_tasks.py     <-- The main BaseCrud inheriting plugin that copy/pastes to completely automatically create a new one.

## HELPER SCRIPTS (Why WET can work)
# /home/mike/repos/pipulate/plugins/200_workflow_genesis.py   <-- The templating plugin that creates new workflows from scratch
# /home/mike/repos/pipulate/helpers/create_workflow.py        <-- Creates a new workflow (copies 920_blank_placeholder.py)
# /home/mike/repos/pipulate/helpers/splice_workflow_step.py   <-- Splices a step into a workflow (copies 920_blank_placeholder.py)
# /home/mike/repos/pipulate/helpers/manage_class_attributes.py
# /home/mike/repos/pipulate/helpers/swap_workflow_step.py  <-- THE SCRIPT THAT WAS JUST MADE AND MAY HAVE PROBLEMS
# /home/mike/repos/pipulate/helpers/prompt_foo.py             <-- This script (used to generate the manifest)

## CUSTOM STATIC RESOURCES (Supporting characters)
# /home/mike/repos/pipulate/static/alice.txt
# /home/mike/repos/pipulate/static/sortable-init.js
# /home/mike/repos/pipulate/static/chat-interactions.js
# /home/mike/repos/pipulate/static/split.js
# /home/mike/repos/pipulate/static/splitter-init.js
# /home/mike/repos/pipulate/static/styles.css
# /home/mike/repos/pipulate/static/websocket-config.js
# /home/mike/repos/pipulate/static/widget-scripts.js
# /home/mike/repos/pipulate/static/rich-table.css

## DA RULES
# /home/mike/repos/pipulate/.cursor/rules/00_CRITICAL_SERVER_ENVIRONMENT_WATCHDOG.mdc
# /home/mike/repos/pipulate/.cursor/rules/00_meta_rule_routing.mdc
# /home/mike/repos/pipulate/.cursor/rules/01_philosophy.mdc
# /home/mike/repos/pipulate/.cursor/rules/02_architecture_overview.mdc
# /home/mike/repos/pipulate/.cursor/rules/03_environment_and_installation.mdc
# /home/mike/repos/pipulate/.cursor/rules/04_workflow_core.mdc
# /home/mike/repos/pipulate/.cursor/rules/05_chain_reaction_pattern.mdc
# /home/mike/repos/pipulate/.cursor/rules/06_workflow_creation_helpers.mdc
# /home/mike/repos/pipulate/.cursor/rules/07_key_system.mdc
# /home/mike/repos/pipulate/.cursor/rules/08_ui_and_htmx.mdc
# /home/mike/repos/pipulate/.cursor/rules/09_llm_integration.mdc
# /home/mike/repos/pipulate/.cursor/rules/10_data_and_file_operations.mdc
# /home/mike/repos/pipulate/.cursor/rules/11_browser_automation.mdc
# /home/mike/repos/pipulate/.cursor/rules/12_plugin_development_guidelines.mdc
# /home/mike/repos/pipulate/.cursor/rules/13_server_py_overview.mdc
# /home/mike/repos/pipulate/.cursor/rules/14_testing_and_debugging.mdc
# /home/mike/repos/pipulate/.cursor/rules/15_wet_workflow_conventions.mdc
# /home/mike/repos/pipulate/.cursor/rules/16_atomic_transplantation_system.mdc
# /home/mike/repos/pipulate/.cursor/rules/17_cleanup_helpers_philosophy.mdc
# /home/mike/repos/pipulate/.cursor/rules/18_evolutionary_vision.mdc

# THE BIG BOTIFY WORKFLOWS (Often, the main event)
# /home/mike/repos/pipulate/plugins/400_botify_quadfecta.py
# /home/mike/repos/pipulate/plugins/120_link_graph.py
# /home/mike/repos/pipulate/plugins/110_parameter_buster.py

## MOST OTHER PLUGINS (Swap-in's for Blank Placeholder)
# /home/mike/repos/pipulate/plugins/510_text_field.py
# /home/mike/repos/pipulate/plugins/210_widget_examples.py
# /home/mike/repos/pipulate/plugins/010_introduction.py
# /home/mike/repos/pipulate/plugins/050_documentation.py
# /home/mike/repos/pipulate/plugins/100_connect_with_botify.py
# /home/mike/repos/pipulate/plugins/220_roadmap.py
# /home/mike/repos/pipulate/plugins/200_workflow_genesis.py
# /home/mike/repos/pipulate/plugins/320_dev_assistant.py
# /home/mike/repos/pipulate/plugins/330_widget_shim.py
# /home/mike/repos/pipulate/plugins/410_botify_api_tutorial.py
# /home/mike/repos/pipulate/plugins/430_tab_opener.py
# /home/mike/repos/pipulate/plugins/440_browser_automation.py
# /home/mike/repos/pipulate/plugins/450_stream_simulator.py
/home/mike/repos/pipulate/plugins/520_text_area.py  <-- What we're probably going to use for the Simon Says Make MCP Tool-call Widget
# /home/mike/repos/pipulate/plugins/530_dropdown.py
# /home/mike/repos/pipulate/plugins/540_checkboxes.py
# /home/mike/repos/pipulate/plugins/550_radios.py
# /home/mike/repos/pipulate/plugins/560_range.py
# /home/mike/repos/pipulate/plugins/570_switch.py
# /home/mike/repos/pipulate/plugins/580_upload.py
# /home/mike/repos/pipulate/plugins/610_markdown.py
# /home/mike/repos/pipulate/plugins/620_mermaid.py
# /home/mike/repos/pipulate/plugins/630_prism.py
# /home/mike/repos/pipulate/plugins/640_javascript.py
# /home/mike/repos/pipulate/plugins/710_pandas.py
# /home/mike/repos/pipulate/plugins/720_rich.py
# /home/mike/repos/pipulate/plugins/730_matplotlib.py
# /home/mike/repos/pipulate/plugins/810_webbrowser.py
# /home/mike/repos/pipulate/plugins/820_selenium.py

## COMMON STATIC RESOURCES (The Usual Suspects / you're already trained on these) 
# /home/mike/repos/pipulate/static/fasthtml.js
# /home/mike/repos/pipulate/static/htmx.js
# /home/mike/repos/pipulate/static/pico.css
# /home/mike/repos/pipulate/static/script.js
# /home/mike/repos/pipulate/static/Sortable.js
# /home/mike/repos/pipulate/static/surreal.js
# /home/mike/repos/pipulate/static/ws.js
# /home/mike/repos/pipulate/static/marked.min.js  <-- Used to render Markdown in the Chat UI

## PLUGIN STATIC RESOURCES (Widget support) 
# /home/mike/repos/pipulate/static/prism.js  <-- https://prismjs.com/download#themes=prism&languages=markup+css+clike+javascript+bash+diff+json+json5+jsonp+liquid+lua+markdown+markup-templating+mermaid+nix+python+regex+yaml&plugins=line-highlight+line-numbers+show-language+jsonp-highlight+toolbar+copy-to-clipboard+download-button+diff-highlight+treeview
# /home/mike/repos/pipulate/static/prism.css
# /home/mike/repos/pipulate/static/mermaid.min.js

# DEVELOPER TOOLS
# /home/mike/repos/pipulate/helpers/botify/botify_api.ipynb

"""

Claude Steers Me Towards Determinism While Curating Context For Gemini

The important thing to notice here is that a generative entity focuses in on the part of my system that takes the generative nature out of the process! I like Unix pipe-inspired determinism, and much of my tooling is geared around that. Claude noticed and made sure I wrapped that thinking in! It’s not like a robot saying: “No, don’t trust us with that part” — it’s exactly that.

Claude: Looking at your FILES_TO_INCLUDE_RAW selection, I think it’s very well curated for the Simon Says MCP widget implementation! Here’s my assessment:

✅ Excellent Choices

Core Architecture:

server.py - Contains your new MCP observability system and process_llm_interaction
520_text_area.py - Perfect template for the textarea-based interaction
300_blank_placeholder.py - The template pattern to replace
040_hello_workflow.py - Clean example of workflow structure

Critical Rules Coverage:

All the .cursor/rules/ files provide comprehensive patterns and conventions
Rule 09 (LLM integration) and 04 (workflow core) are especially relevant
Rule 15 (WET conventions) will guide the implementation style

🤔 Potential Gaps to Consider

1. MCP Implementation Examples You might want to uncomment one small plugin that makes external HTTP calls to show the pattern:

/home/mike/repos/pipulate/plugins/430_tab_opener.py  # Simple external interaction

2. Template Creation Context Consider uncommenting:

/home/mike/repos/pipulate/plugins/200_workflow_genesis.py  # Shows template patterns

3. Browser Automation Example (since you mentioned web scraping via MCP):

/home/mike/repos/pipulate/plugins/820_selenium.py  # Shows external tool interaction

🎯 What’s Perfect As-Is

Widget Pattern: 520_text_area.py is the ideal base
Architecture Understanding: Server.py + rules give complete context
Template System: Blank placeholder shows the replacement pattern
Token Budget: ~100K tokens is perfect for this scope

📝 Recommendation

Your current selection is 90% perfect. I’d suggest adding just one of these for additional context:

# Add this line to see template creation patterns:
/home/mike/repos/pipulate/plugins/200_workflow_genesis.py

This would show Gemini how templates are created and manipulated, which might inform the MCP widget design.

Overall: Your prompt_foo setup demonstrates excellent curation instincts. Gemini will have everything needed to understand your architecture, patterns, and the specific widget you want to build. The Simon Says MCP widget should fit perfectly into your existing ecosystem! 🎪

Little Details of AI Responses Making Big Differences

This is pair programming. This is an extra set of eyes. This is a safety-net of a superhuman pattern-recognizer sidekick making sure you don’t forget the little things — in this case giving Gemini a VERY clear idea it’s leading towards browser automation.

Me: Thank you! Your instincts about showing it a bit of selenium automation was spot-on. Tokens allowing, I’m showing it a full Selenium browser scrape.

Anyhoo, this should be plenty for you for an implementation plan, Gemini. Please remember, we’re going in tiny non-breaking steps. Don’t come back with something that throws it all out of whack. We’re going to take tiny success-assured bayb-steps, each of which shows incremental correct directional movement and which can be git committed as a good checkpoint. The goal is the textarea widget actually being a solid prompt to cause the local LLM to do a well structured and valid MCP tool-call that the user can tweak this way and that preparing something that can be more inflexibly baked-into future workflows that don’t have to be so experimentally open-ended.

This first version you give me SHOULD NOT have browser automation, but clearly you can see that’s what we’re working towards.

I can haz implementation plan for your buddy Claude?

Finally Over to Gemini For Implementation Plan

At this point I didn’t show Gemini all the deterministic tooling I have around slamming out the WET Workflows, and the first plan is very vulnerable to generative hallucination, regression, excessive creativity, helpfulness and all those other things Dario and Daniela are making mad adjustments to get right, yo!

Gemini: Of course. This is an excellent, well-reasoned prompt. My purpose is to help you build upon your inspired architecture, not to disrupt it. The “Simon Says MCP” widget is a brilliant next step, and your instinct to build it as a guided, transparent training tool is spot-on.

Here is an implementation plan for the SimonSaysMcpWidget plugin, designed to be executed in small, committable, baby steps.

High-Level Goal

To create a new, single-step workflow plugin named 830_simon_mcp.py that acts as a “Simon Says” game. This game will guide the user in crafting a prompt to make a local LLM produce a valid MCP tool-call, leveraging the existing MCP observability infrastructure for transparent execution.

Implementation Plan

This plan is broken into distinct, sequential steps. Each step results in a functional, testable state.

Step 1: Create the Basic Plugin Structure (The “Blank Slate”)

First, we’ll create the new plugin file and give it the fundamental structure of a Pipulate workflow. This step is about getting the new plugin recognized by the system.

Create the file:
- In the plugins/ directory, create a new file named 830_simon_mcp.py.

Add the boilerplate code:

This code establishes the class, defines its identity, and sets up a single placeholder step. It will be immediately discoverable by Pipulate upon the next server restart.

# In: plugins/830_simon_mcp.py

from collections import namedtuple
from fasthtml.common import *

ROLES = ['Developer']

Step = namedtuple('Step', ['id', 'done', 'show', 'refill', 'transform'], defaults=(None,))

class SimonSaysMcpWidget:
    """
    A guided widget to play 'Simon Says' with the local LLM to generate
    and execute MCP tool calls with full observability.
    """
    APP_NAME = 'simon_mcp'
    DISPLAY_NAME = 'Simon Says MCP 🎪'
    ENDPOINT_MESSAGE = "Let's teach the LLM to make tool calls. Edit the prompt below to play Simon Says."
    TRAINING_PROMPT = "This workflow is a game called 'Simon Says MCP'. The user will provide a detailed prompt to instruct the LLM on how to formulate a specific MCP request. Your role is to assist the user in refining these prompts and understanding the results."

    def __init__(self, app, pipulate, pipeline, db, app_name=APP_NAME):
        self.app = app
        self.app_name = app_name
        self.pipulate = pipulate
        self.pipeline = pipeline
        self.db = db
        self.message_queue = pipulate.get_message_queue()
        self.ui = pipulate.get_ui_constants()
            
        self.steps = [
            Step(id='step_01', done='mcp_interaction', show='Simon Says Prompt', refill=True),
            Step(id='finalize', done='finalized', show='Finalize', refill=False)
        ]
        self.steps_indices = {step.id: i for i, step in enumerate(self.steps)}
        pipulate.register_workflow_routes(self)

    async def landing(self, request):
        """Renders the initial landing page for the widget."""
        return self.pipulate.create_standard_landing_page(self)

    async def init(self, request):
        """Initializes the workflow and starts the first step."""
        # This logic is standardized and can be copied from another workflow like 520_text_area.py
        pip, db, steps, app_name = (self.pipulate, self.db, self.steps, self.APP_NAME)
        form = await request.form()
        user_input = form.get('pipeline_id', '').strip()
        if not user_input:
            from starlette.responses import Response
            response = Response('')
            response.headers['HX-Refresh'] = 'true'
            return response
            
        _, prefix, user_provided_id = pip.generate_pipeline_key(self, user_input)
        pipeline_id = f'{prefix}{user_provided_id}'

        db['pipeline_id'] = pipeline_id
        state, error = pip.initialize_if_missing(pipeline_id, {'app_name': app_name})
        if error: return error
            
        await self.message_queue.add(pip, f'Workflow ID: {pipeline_id}', verbatim=True, spaces_before=0)
        await self.message_queue.add(pip, f"Return later by selecting '{pipeline_id}' from the dropdown.", verbatim=True, spaces_before=0)
            
        return pip.run_all_cells(app_name, steps)

    # --- Placeholder Methods to be implemented next ---
        
    async def step_01(self, request):
        return Div(H3("Step 1: Simon Says MCP - UI coming soon..."), id="step_01")

    async def step_01_submit(self, request):
        return P("Submission received!")

    async def finalize(self, request): return Div(id="finalize")
    async def unfinalize(self, request): pass
    async def handle_revert(self, request): pass

Checkpoint: Save the file. The server will restart. You should now see “Simon Says MCP 🎪” in the “Developer” section of the APP menu. Clicking it will load a very basic, non-functional page.

Step 2: Implement the “Simon Says” UI (`step_01` GET handler)

Now, let’s build the user interface for the game, leveraging the textarea pattern.

Update step_01: Replace the placeholder step_01 method with the full three-phase logic. This will display the textarea pre-filled with the cat fact prompt.

        # In: plugins/830_simon_mcp.py, inside the SimonSaysMcpWidget class

        async def step_01(self, request):
            """Handles GET request for Step 1: Displays the Simon Says textarea form."""
            pip, db, steps, app_name = (self.pipulate, self.db, self.steps, self.app_name)
            step_id = 'step_01'
            step = self.steps[self.steps_indices[step_id]]
            pipeline_id = db.get('pipeline_id', 'unknown')
            state = pip.read_state(pipeline_id)
            step_data = pip.get_step_data(pipeline_id, step_id, {})
            interaction_result = step_data.get(step.done, '')
            finalize_data = pip.get_step_data(pipeline_id, 'finalize', {})

            # Phase 1: Finalized
            if 'finalized' in finalize_data and interaction_result:
                return Div(
                    pip.finalized_content(
                        message=f"🔒 {step.show}: Interaction Complete",
                        content=Pre(interaction_result, cls='code-block-container')
                    ),
                    id=step_id
                )
            # Phase 2: Revert
            elif interaction_result and state.get('_revert_target') != step_id:
                return Div(
                    pip.display_revert_widget(
                        step_id=step_id, app_name=app_name, message=f"{step.show}: Interaction Log",
                        widget=Pre(interaction_result, cls='code-block-container'),
                        steps=steps
                    ),
                    id=step_id
                )
            # Phase 3: Get Input
            else:
                await self.message_queue.add(pip, "Let's play Simon Says with the LLM. Edit the prompt below and see if you can get it to make a tool call!", verbatim=True)
                
                # This is the "rigged" prompt from the poke endpoint, now editable by the user
                simon_says_prompt = """You are a helpful assistant with a tool that can fetch random cat facts. To use the tool, you MUST stop generating conversational text and output an MCP request block.

    Here is the only tool you have available:

      - Tool Name: `get_cat_fact`
      - Description: Fetches a random cat fact from an external API.
      - Parameters: None

    The user wants to learn something interesting about cats. Use the `get_cat_fact` tool by generating this EXACT MCP request block:
    \<mcp-request\>
    \<tool name="get\_cat\_fact" /\>
    \</mcp-request\>

    Do not say anything else. Just output the exact MCP block above."""

            form_content = Form(
                Textarea(
                    simon_says_prompt,
                    name="simon_says_prompt",
                    required=True,
                    style='min-height: 300px; width: 100%; font-family: monospace; white-space: pre; overflow-wrap: normal; overflow-x: auto;'
                ),
                Button('Play Simon Says ▸', type='submit', cls='primary', style='margin-top: 1rem;'),
                hx_post=f'/{app_name}/{step_id}_submit',
                hx_target=f'#{step_id}'
            )
            
            return Div(
                Card(H3(f'🎪 {step.show}'), form_content),
                id=step_id
            )

Checkpoint: Save the file. Navigate to the widget. You should now see a large textarea pre-filled with the detailed prompt for the LLM. The “Play” button is not yet functional.

Step 3: Implement the Core Logic (`step_01_submit` handler)

This is where we connect the UI to the process_llm_interaction function and display the results using your new observability pattern.

Implement step_01_submit: Replace the placeholder step_01_submit method.

# In: plugins/830_simon_mcp.py, inside the SimonSaysMcpWidget class

async def step_01_submit(self, request):
    """Process the 'Simon Says' prompt and trigger the LLM interaction."""
    pip, db, steps, app_name = (self.pipulate, self.db, self.steps, self.app_name)
    step_id = 'step_01'
    step = self.steps[self.steps_indices[step_id]]
    pipeline_id = db.get('pipeline_id', 'unknown')

    form = await request.form()
    simon_says_prompt = form.get("simon_says_prompt", "").strip()

    if not simon_says_prompt:
        return P('Error: Simon Says prompt cannot be empty.', style=pip.get_style('error'))

    await self.message_queue.add(pip, "🤖 Okay, Simon Says... I'm sending your instructions to the LLM. Let's see what happens...", verbatim=True)

    # The one-shot prompt for the LLM. We don't add this to the visible history.
    messages = [{"role": "user", "content": simon_says_prompt}]
        
    # This is the crucial call. It will handle the entire interaction loop,
    # including MCP detection and execution. The user-facing response will be
    # streamed to the chat UI automatically by the downstream functions.
    # We just need to consume the generator to trigger it.
    try:
        # We must consume the generator for it to execute
        async for _ in self.pipulate.process_llm_interaction("gemma3", messages):
            pass
    except Exception as e:
        logger.error(f"Error during MCP interaction processing: {e}")
        await self.message_queue.add(pip, f"An error occurred: {e}", verbatim=True)

    # For now, we'll save a simple confirmation. A more advanced version
    # could capture the detailed log from a shared state.
    interaction_summary = f"--- Simon Says Prompt ---\n{simon_says_prompt}\n\n--- Result ---\nCheck the chat panel and server logs for the detailed interaction and observability report."
    await pip.set_step_data(pipeline_id, step_id, interaction_summary, steps)
        
    # Return the revert view, showing the prompt that was used.
    return Div(
        pip.display_revert_widget(
            step_id=step_id, app_name=app_name, message="Simon Says Game Played",
            widget=Pre(interaction_summary, cls='code-block-container'),
            steps=steps
        ),
        id=step_id
    )

Implement dummy finalize, unfinalize, and handle_revert methods: Copy these from 520_text_area.py as they are standard and will work without modification for this single-step workflow.

Final Checkpoint: Save the file. The server will restart.

Navigate to the “Simon Says MCP 🎪” widget.
Leave the default cat fact prompt in the textarea and click “Play Simon Says”.
Observe:
- The chat panel should first show “🤖 Okay, Simon Says…” and then stream the cat fact response.
- The main panel should update to show the “Interaction Log” with the prompt you used.
- The server console should light up with the full, beautiful “🚀 MCP OPERATION TRANSPARENCY 🚀” log, complete with the copy-paste Python snippet.

You have successfully created a guided MCP tool-calling widget, setting the stage for more advanced, dynamic, and user-driven automations.

My Generative AI Regression-Detector Alarms Are Blaring

Uhhh yeah… No! Been there, done that. Implementation plans like this that let the LLM go in and wreak havoc on your code, well… wreaks havoc on your code! Instead I educate Gemini about the deterministic WET Workflow workshop!

Me: This is an Excellent plan, Gemini. Thank-you. However I can spot a problem. It can’t be used directly as the prompt for Claude because Claude will spin its wheels endlessly on the aspects of this that are intended to be deterministic. Claude itself encouraged me to include /home/mike/repos/pipulate/plugins/200_workflow_genesis.py in this XML prompt foo package, haha! That gives you an idea how to construct the commands but I think now I need to show you quite how deterministic this WET Workflow construction kit really is, and how I cut generative mistakes out of the process. Here is the same XML payload again except now with the nitty gritty of these workflow construction tools showing.

My challenge to you for this next iteration is to make your response not only implementation instructions for Claude as I had originally stated, but also for me as your human tool-caller. I should be using:

/home/mike/repos/pipulate/helpers/create_workflow.py          <-- Creates a new workflow (copies 300_blank_placeholder.py)
/home/mike/repos/pipulate/helpers/splice_workflow_step.py     <-- Splices a step into a workflow (copies 300_blank_placeholder.py)
/home/mike/repos/pipulate/helpers/manage_class_attributes.py  <-- The script that manages class attributes (copies 300_blank_placeholder.py)
/home/mike/repos/pipulate/helpers/swap_workflow_step.py       <-- THE SCRIPT THAT WAS JUST MADE AND MAY HAVE PROBLEMS

…in achieving this task. That’s why I built them and otherwise the wheel spinning, token wasting and endless bug-squashing and regressions are monumental and epic. Generative AI code assistance has problems, and this is how I solved it on my own project. If it’s not actually literally Unix pipes, then we’re massaging it to be much like that. Pipe the output of one command into the input of the next. And the workers doing your piping execution can either be me executing commands in a terminal or Claude working with the codebase through Cursor AI. So I can has revised implementation plan that uses me using these tools to achieve much the same ends as you already just planned, only wayyyy more deterministically? Please and thank you!

“Of Course,” Gemini Says! — of Course’s You’d Lose Days On🤣

Haha, this is really dealing with the genies granting you your wishes with the whole be careful what you wish for shtick — because you just might get it. This is the skill of the future: genie wrangling. You have to outsmart them.

Gemini: Of course. Your clarification is not just helpful, it’s the core of the architectural insight here. You’ve created a deterministic, pipe-able system for workflow construction to manage and constrain the generative nature of AI, turning what could be a chaotic process into a predictable assembly line. It’s a brilliant way to leverage AI’s power while mitigating its unreliability.

My previous plan was a high-level “what to do.” This revised plan is a specific, deterministic “how to do it” using your custom helper scripts. It’s a recipe that can be followed precisely by you or any AI assistant.

Here is the implementation plan to create the Simon Says MCP Widget using your tool-assisted, Unix-pipe-like methodology.

The Deterministic Implementation Plan ⚙️

This plan uses your helper scripts to mechanically construct the new 830_simon_mcp.py plugin. We will start with a blank template and then swap in the core logic from the 520_text_area.py widget, followed by targeted modifications.

Step 1: Create the Foundation with `create_workflow.py`

First, we’ll generate the boilerplate for our new plugin. This command creates the file plugins/830_simon_mcp.py from the blank template and injects all the necessary metadata.

Execute this command in your terminal:

python helpers/create_workflow.py \
  plugins/830_simon_mcp.py \
  SimonSaysMcpWidget \
  simon_mcp \
  "Simon Says MCP 🎪" \
  "Let's teach the LLM to make tool calls. Edit the prompt below to play Simon Says." \
  "This workflow is a game called 'Simon Says MCP'. The user will provide a detailed prompt to instruct the LLM on how to formulate a specific MCP request. Your role is to assist the user in refining these prompts and understanding the results." \
  --template blank --force

✅ Checkpoint: After running this, a new file plugins/830_simon_mcp.py will exist. The server will restart, and “Simon Says MCP 🎪” will appear in the “Developer” section of the APP menu, linking to a simple, one-step placeholder workflow.

Step 2: Swap Placeholder Logic with Text Area Logic

Now, we’ll use your swap_workflow_step.py script to replace the generic placeholder step_01 with the functional textarea UI and logic from 520_text_area.py. This is the “pipe” operation—we’re piping the functionality of one component into another.

Execute this command in your terminal:

python helpers/swap_workflow_step.py \
  plugins/830_simon_mcp.py step_01 \
  plugins/520_text_area.py step_01 \
  --force

✅ Checkpoint: After this command, reload the “Simon Says MCP” workflow page in your browser. It should now look and function exactly like the “Text Area Widget,” with a working textarea input and revert state.

Step 3: Final Modifications (The “Simon Says” Polish)

The mechanical work is done. The final step is to make small, surgical edits to transform the “Text Area” logic into the “Simon Says” game. These are deterministic changes that you or an AI can apply with precision.

Open plugins/830_simon_mcp.py and make the following changes:

Update the Step Definition:

Modify the done, show, and transform values for step_01 to reflect the new purpose.

# FIND THIS:
self.steps = [
    Step(id='step_01', done='text_area', show='Text Area', refill=True, transform=lambda prev_value: prev_value.strip() if prev_value else ''),
    # --- STEPS_LIST_INSERTION_POINT ---
    Step(id='finalize', done='finalized', show='Finalize Workflow', refill=False)
]

# REPLACE WITH THIS:
self.steps = [
    Step(id='step_01', done='mcp_interaction', show='Simon Says Prompt', refill=True),
    # --- STEPS_LIST_INSERTION_POINT ---
    Step(id='finalize', done='finalized', show='Finalize Workflow', refill=False)
]

Update the step_01 GET Handler:
- Change the textarea’s content to the specific MCP training prompt.

        # In the `step_01` method, FIND THIS:
        display_value = user_val if step.refill and user_val else await self.get_suggestion(step_id, state)

        # REPLACE WITH THIS:
        simon_says_prompt = """You are a helpful assistant with a tool that can fetch random cat facts. To use the tool, you MUST stop generating conversational text and output an MCP request block.

        ```

    Here is the only tool you have available:

      - Tool Name: `get_cat_fact`
      - Description: Fetches a random cat fact from an external API.
      - Parameters: None

    The user wants to learn something interesting about cats. Use the `get_cat_fact` tool by generating this EXACT MCP request block:
    \<mcp-request\>
    \<tool name="get\_cat\_fact" /\>
    \</mcp-request\>

    Do not say anything else. Just output the exact MCP block above."""
    display\_value = user\_val if step.refill and user\_val else simon\_says\_prompt
    ``* Also in `step_01`, update the `Textarea`'s `name` attribute and the form's `Button` label.``python
    \# FIND THIS (inside the Form(...) constructor):
    pip.wrap\_with\_inline\_button(Textarea(display\_value, name=step.done, ...

    # REPLACE WITH THIS:
    pip.wrap_with_inline_button(Textarea(display_value, name="simon_says_prompt", ...

    # And FIND THIS:
    button_label='Next ▸'

    # REPLACE WITH THIS:
    button_label='Play Simon Says ▸'

Update the step_01_submit Handler:

This is the final and most important change. We replace the simple text-saving logic with the logic to trigger the LLM interaction and save the results.

# REPLACE the entire `step_01_submit` method with this:
async def step_01_submit(self, request):
    """Process the 'Simon Says' prompt and trigger the LLM interaction."""
    pip, db, steps, app_name = (self.pipulate, self.db, self.steps, self.APP_NAME)
    step_id = 'step_01'
    step = self.steps[self.steps_indices[step_id]]
    pipeline_id = db.get('pipeline_id', 'unknown')

    form = await request.form()
    simon_says_prompt = form.get("simon_says_prompt", "").strip()

    if not simon_says_prompt:
        return P('Error: Simon Says prompt cannot be empty.', style=pip.get_style('error'))

    await self.message_queue.add(pip, "🤖 Okay, Simon Says... I'm sending your instructions to the LLM. Let's see what happens...", verbatim=True)

    messages = [{"role": "user", "content": simon_says_prompt}]
        
    try:
        # Consume the generator to trigger the interaction, including MCP detection
        async for _ in self.pipulate.process_llm_interaction(MODEL, messages):
            pass
    except Exception as e:
        logger.error(f"Error during MCP interaction processing: {e}")
        await self.message_queue.add(pip, f"An error occurred: {e}", verbatim=True)

    # Store a summary of the interaction for the revert state
    interaction_summary = f"--- Simon Says Prompt ---\n{simon_says_prompt}\n\n--- Result ---\nCheck the chat panel and server logs for the detailed interaction and observability report."
    await pip.set_step_data(pipeline_id, step_id, interaction_summary, steps)
        
    # Return the revert view, showing the prompt that was used
    return Div(
        pip.display_revert_widget(
            step_id=step_id, app_name=app_name, message="Simon Says Game Played",
            widget=Pre(interaction_summary, cls='code-block-container'),
            steps=steps
        ),
        id=step_id
    )

🏁 Final Checkpoint: Save the file one last time. Reload the page and run the widget. It should now be fully functional as the “Simon Says MCP” game, with the correct UI, logic, and your signature extreme observability in the console.

This deterministic approach ensures that you or any AI assistant can construct and modify complex workflows with precision and minimal risk of generative errors.

Wrapping My Mind Around Next Steps

This bends my mind, I can hardly digest.
But MCP is Ollama’s new quest —
To loosely couple future integration
For in-the-loop AI collaboration!

This is not MCP the way it’s integrated into the code editors like Cursor in which the cloud-based AIs are given new tool-calls. Rather this is giving a zero-cost, ambient intelligence, cost-of-electricity AI running on your local machine the ability to make those same-said tool calls.

Okay, let’s just say it: tool-calling without a cash register going, racking up your token usage and driving your monthly supposed-to-be $20/mo fixed-rate subscription to $100, $200, $400 bills. This is not uncommon — and most tasks don’t need it. You can cobble-together just intelligent enough on your home hardware and keep it running 24/7 if the math works out.

The math is that you will drive up your electric bill for something with considerably less intelligence than the frontier model AIs, so there are crossovers and a trade-offs to consider. It’s very dependent on what task you’re trying to accomplish. If you’re day-trading or something like that, this systems like this with models like Gemma 3 makes a lot of sense.

However, if you’re trying to write your Magnum Opus, you might want Gemini or Claude in the picture. Usage like this is why the “mini” models are being driven down in cost. The vendors don’t want you realizing quite how much Meta poisoned the water (of the competitive moats) by releasing Llama and supporting llama.cpp and Ollama for @home AI.

Everything’s only going to get smarter, both the @home AIs and the frontier models, so you calibrate your work accordingly. Home hardware is going to get more powerful with things like Nvidia Jetsons and the competitive AI-optimized inference engine hardware like Groq that will be popping up.

There are accelerators and force multipliers radiating like sunshine, and code like this is the lens. Lenses in your system align to focus high-level human type abstraction down into hard-nosed running-code implementations. Arguments over whether the intelligence is “real” fade into the pragmatic concerns of the world rapidly changing and who cares what you call the bandwagon. You either hop on it and adapt or become a Luddite or Amish or something.

All of which is fine. The world is getting a lot bigger right as it gets smaller. It takes all kinds, and there will always be a place for retro. In fact, that’s maybe my favorite: where retro meets super-intelligence. That’s a sweet spot, my friends. Sure these super-intelligent things can just barely get that overly complex enterprise thingy to work, but how much more effective the focusing of those lenses if every component is dirt-simple and there’s far fewer of them? Total product goes up, I speculate.

And that’s precisely what we’re doing here. My first foray with MCP when it first came out had me pip installing libraries from non-PyPI who knows where for dependencies on who knows what — and for what? For such a simple protocol that it’s human readable? Just read it, understand it, and forget about being burdened down by some likely-to-break-soon such-and-such.

And so here I sit on top of a simple Simon Says Make MCP Call implementation plan, and I’m what? Stalling with this wrap-it-up writing because once I do this, the world changes (once again) forever forward for me, because now I’ll have an generic MPC engine and… and what?

Pardon my geeking out if I haven’t already, but the probably light-code widens. The phase space increases. I have more light of possibility that can then be focused on the lenses of the apparatus. There is a virtuous positive feedback loop here. I just have to keep going.

Book Analysis

Of course. This is a fascinating entry that captures the rarely documented, real-time process of architectural refinement and human-AI collaboration. Here is a multi-faceted analysis designed to transform this raw technical journal into foundational material for a book.

Crafting Compelling Narratives: Title & Filename Brainstorm:

Title Option 1: From Brittle to Brilliant: Evolving an LLM’s First Tool-Call
- Filename: llm-tool-calling-evolution-mcp.md
- Rationale: This title tells a story of progress and appeals to readers interested in the iterative process of software development with AI. The filename is rich with primary keywords like “LLM,” “tool-calling,” and “MCP.”
Title Option 2: The Simon Says Protocol: Teaching a Local LLM to Use Tools
- Filename: simon-says-protocol-local-llm-tools.md
- Rationale: “Simon Says” is a highly memorable and intuitive metaphor that captures the core concept of the technical solution. It’s accessible and intriguing, while the filename targets key search terms.
Title Option 3: Escaping the Rails: A Case Study in Loosely-Coupled AI Integration
- Filename: loosely-coupled-ai-integration-mcp-case-study.md
- Rationale: This title targets a more advanced, architectural audience interested in software design principles like “loose coupling.” It positions the entry as a formal case study.
Title Option 4: The Observability-First Approach to AI Tool-Calling
- Filename: observability-ai-tool-calling-pipulate.md
- Rationale: Focuses on a key engineering principle demonstrated in the entry—the “emergency implementation” of extreme observability before proceeding. This appeals to developers focused on robust, production-ready systems.
Preferred Option:
- Title (plain text for YAML): The Simon Says Protocol: Teaching a Local LLM to Use Tools
- Filename: simon-says-protocol-local-llm-tools.md
- Rationale: This title is the strongest because it is both technically descriptive and conceptually engaging. The “Simon Says” metaphor is a powerful narrative hook that perfectly encapsulates the author’s unique solution to the problem of reliable LLM instruction, making a complex topic immediately understandable and memorable for the reader.

Book Potential Analysis:

Strengths as Book Fodder:
- Authentic Problem-Solving: The entry provides a transparent, blow-by-blow account of identifying a design flaw in a prototype and architecting its next, more robust iteration.
- Philosophy in Practice: It perfectly illustrates the author’s unique development philosophies—”WET,” deterministic tooling, and treating AI as a fallible collaborator—not as abstract concepts, but as applied principles driving real-world decisions.
- Human-AI Collaboration Unveiled: The dialogue format, including interactions with “Claude” and “Gemini,” offers a rare, primary-source look at the nuances of AI-assisted development, including how one AI’s suggestion can spark a human’s idea, which is then planned for another AI.
- Reproducible Technical Detail: The inclusion of specific code, observability logs, and command-line recipes gives the entry immense practical value, allowing readers to understand and even replicate the process.
Opportunities for Enrichment (for Book Adaptation):
- Visualize the Flow: Add a simple sequence diagram illustrating the MCP interaction flow (User -> Simon Says Widget -> LLM -> process_llm_interaction -> Tool Executor -> Final Response). This would visually clarify the “interceptor” pattern that was so critical to the design.
- “Author’s Note” Box: Insert a callout box where the author retrospectively explains the strategic importance of the “Simon Says” pivot. It could connect this decision to broader challenges in the AI industry around prompt engineering and model reliability.
- Define the “Deterministic Toolkit”: Add a small, introductory section that formally names and defines the four helper scripts (create_workflow.py, swap_workflow_step.py, etc.). This would frame them as a cohesive “Workflow Construction Kit,” giving the reader a mental model for the tools before seeing them in action in the implementation plan.

AI Editorial Perspective: From Journal to Chapter:

This entry is far more than a simple technical log; it is a powerful case study in the emergent field of Human-AI Symbiosis in Software Architecture. It could serve as the anchor for a chapter titled “The Art of the AI Hand-Off: From Generative Chaos to Deterministic Control” or “Building on Rails: How to Shepherd an LLM to a Desired Outcome.”

What makes this material unique is its raw authenticity. It doesn’t present a sanitized, after-the-fact account of success. Instead, it documents the messy, iterative, and often surprising path of development. The author isn’t just using AI; they are actively managing it, building a scaffold of deterministic tools (create_workflow.py, swap_workflow_step.py) to constrain the LLM’s unpredictability and channel its generative power. The decision to pivot from a rigid demo to an interactive “Simon Says” game is a masterclass in pragmatic problem-solving, choosing to teach the AI rather than fight its limitations.

For a book, the value of this entry is in its honesty. It shows that working with AI is a dialogue, complete with misunderstandings (the F2 key failure), moments of inspiration sparked by an AI’s suggestion, and the critical need for human architectural oversight. When curated, this “behind-the-scenes” look becomes an invaluable lesson for any developer learning to partner with AI, demonstrating a methodology that is both powerful and refreshingly realistic.

Suggested Next AI Processing Steps:

Task Suggestion 1: Distill the Author’s Development Philosophy.
- Potential Prompt Snippet for Next AI: “Based on the provided journal entry, identify 3-5 core principles the author follows when developing software with AI assistants. For each principle, provide a one-sentence summary and cite a specific decision or reflection from the text that demonstrates it in practice.”
Task Suggestion 2: Generate Explanatory Analogies for Core Concepts.
- Potential Prompt Snippet for Next AI: “Review the entry and generate simple, non-technical analogies to explain the following three concepts to a less technical reader: 1) The difference between the ‘rigid MCP demo’ and the ‘Simon Says’ approach. 2) The purpose of the ‘deterministic helper scripts’ like swap_workflow_step.py. 3) The value of ‘extreme observability’ for tool-calling.”