Month: June 2025

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.