Smart Poi

MCP is the new AI buzzword. Being a bit involved in the AI-enhanced programming of my own projects, it escaped my attention until about a week ago. It’s time to have a look.

What I am using

Since I don’t want to pay any money (MCP can drain your tokens pretty quick!) I tried setting this up using local models first – but they are very slow on my laptop and I went with Deepseek Chat which is cheap for this test.

Essential Programs

1. Ollama – run LLM’s on your own computer
2. MCP Client for Ollama – allows your local models to connect to MCP’s and for you to configure and control everything from the command line OR:
3. ChatMCP – cross platform GUI program for chatting to MCP enhanced LLM’s. Configure any LLM from api (Deepseek, Claude, OpenAI) to Ollama.
4. MCP’s – there are literally thousands of these already! Some lists I found:
   – https://github.com/modelcontextprotocol/servers
   – https://glama.ai/mcp/servers
5. DeepSeek – get your api key (or sign up for OpenRouter and use the free rate limited one!)
   
   
   

Example using ChatMCP

I will be using this simple calculator MCP as an example:
– https://github.com/githejie/mcp-server-calculator
I just happened to have qwen2.5-coder:1.5b already installed in Ollama so that’s the one I am using (it supports tools) actually I used Deepseek Chat – Ollama is a bit slow on my laptop (it does work though).

In ChatMCP we add the tool like so:

After configuring my Deepseek API key in the settings (bottom right) I choose it from the menu.

DeepSeek Chat works fine (and it’s cheaper). I also got qwen2.5-coder to call tools, it’s a bit slow on my laptop, however (requires Ollama to be running in the background and I don’t have a GPU).

You need to enable the tool:

Then just make the request:

As you can see the AI used the calculator tool (spanner icon) to answer the request! There are so many tools available, from web scraping to controlling your android phone! I even made my own MCP tool to turn on an LED.

I just took a photo with my Android phone by telling the AI to do it for me (using phone-mcp)! What will your MCP enabled AI assistant be able to do?

NOTES

You can add MCP tools to your coding assistant now (eg. Cursor). I am using Cline which has a plugin for VSCode and allows for Deepseek API use (I already pay for this). The configuration looks like this (same format for “MCP Client for Ollama”):

{
  "mcpServers": {
    "hello-world-server": {
      "disabled": false,
      "timeout": 60,
      "command": "/run/media/tom/9109f38b-6b5f-4e3d-a26f-dd920ac0edb6/Manjaro-Home-Backup/3717d0b5-ba54-4c0a-8e8d-407af5c801bd/@home/tom/Documents/PROGRAMMING/Python/mcp_servers/hello_world/.venv/bin/python",
      "args": [
        "-u",
        "/run/media/tom/9109f38b-6b5f-4e3d-a26f-dd920ac0edb6/Manjaro-Home-Backup/3717d0b5-ba54-4c0a-8e8d-407af5c801bd/@home/tom/Documents/PROGRAMMING/Python/mcp_servers/hello_world/server_mcp.py"
      ],
      "env": {
        "PYTHONUNBUFFERED": "1"
      },
      "transportType": "stdio"
    },
    "blink-led-server": {
      "disabled": false,
      "timeout": 60,
      "command": "/run/media/tom/9109f38b-6b5f-4e3d-a26f-dd920ac0edb6/Manjaro-Home-Backup/3717d0b5-ba54-4c0a-8e8d-407af5c801bd/@home/tom/Documents/PROGRAMMING/Python/mcp_servers/mcp_duino/.venv/bin/python",
      "args": [
        "/run/media/tom/9109f38b-6b5f-4e3d-a26f-dd920ac0edb6/Manjaro-Home-Backup/3717d0b5-ba54-4c0a-8e8d-407af5c801bd/@home/tom/Documents/PROGRAMMING/Python/mcp_servers/mcp_duino/server_mcp.py"
      ],
      "env": {},
      "transportType": "stdio"
    },
    "github.com/modelcontextprotocol/servers/tree/main/src/puppeteer": {
      "disabled": false,
      "timeout": 60,
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--init",
        "-e", "DOCKER_CONTAINER=true",
        "-e", "DISPLAY=$DISPLAY",
        "-v", "/tmp/.X11-unix:/tmp/.X11-unix:rw",
        "--security-opt", "seccomp=unconfined",
        "mcp/puppeteer",
        "--disable-web-security",
        "--no-sandbox",
        "--disable-dev-shm-usage"
      ],
      "env": {},
      "transportType": "stdio"
    },
    "phone-mcp": {
      "command": "uvx",
      "args": [
        "phone-mcp"
      ]
    },
    "calculator": {
      "command": "uvx",
      "args": [
        "mcp-server-calculator"
      ]
    }
  }
}

As you can see, uvx solves a lot of configuration long story here – otherwise you have to specify the path of your virtual environment.

The most common MCP servers are Node based, or Python. I am using Python as it’s my preferred language. Node is pretty similar, just use npx instead of uv.

Next Steps

Next up: converting all of my code to work with MCP. Seriously – if you aren’t MCP compatible, then you need to work on it, I think in the future this will be very important. Check out FastMCP for python implementation.

I use the open source and very capable Aider for AI code generation. Anyone who has tried AI code generation has heard of “context” – the information you send over to the LLM so that it can know what the code looks like that you want to modify.

Anyone using these tools eventually comes up against the dreaded token limit at some point – some sooner rather than later! The LLM can only keep so much context in memory at one time. This is annoying if you are trying to update a legacy codebase with multiple files and countless lines of code – who wants to try to pare this down to only the relevant parts, copy and paste into the window?

In Aider adding files to context is as simple as /add file.txt
To remove files just do /drop file.txt
If file.txt is 100 000 lines long you have to copy and paste the parts you want (up to 60 000 tokens), check how many you have left by running /tokens
But that is takes too long! It defeats the purpose of getting AI to do the reading and modifying for you (remember to review the diffs after!)

Meet LLMAP

llmap is “context extraction at scale”. The tool can search and sumarise vast amounts of code and ouput only the relevant parts which you can then add to your AI coding tool context – leaving out all irrelevant parts (usually most of it!)
Example from my own use case: I had an issue with a looping api request on ESP32 in my Magic Poi project – I had been concentrating on the battery monitor feature and the new feature broke some other functionality. So I did a diff with the last known working branch:
git diff origin/Battery_Monitor_Main_Merge > context_git_diff.txt

This diff is from way back though so it was too large for the context window (if you include all of the code I wanted to update) so I had to run it through llmap with a query:
echo context_git_diff.txt | llmap "list the changes made that affect the control flow of the application." > llmap_diff_context.txt

Now my new file llmap_diff_context.txt with only the relevant information could be added to the context – using /read-only since it’s not included in git nor do I want to edit it. I used /architect mode* to see what changes happened to cause the loop. Turns out it was a simple misplaced line of code and everything worked again!
*for me architect mode is configured to use DeepSeek R1 for thinking and V3 for editing – cheap and effective

LLMAP is easy to use – just install via pip. By the way, if you don’t have a DeepSeek api key, the update I recently submitted to add OpenRouter support has been merged but not yet published. You will have to download and install llmap manually to use OpenRouter – but it works.

Full credit to Johnathan Ellis for creating and sharing this great tool. You gotta love Open Source!