Tracker MCP Server
Enables LLM clients to interact with a local tracker-server for checking schedule, retrieving statistics, registering tasks, and managing timers.
README
Tracker MCP Server
This is an MCP (Model Context Protocol) server designed to connect to your local tracker-server. It exposes endpoints like checking today's schedule, retrieving statistics, registering tasks, and managing timers directly to LLM clients (like Claude Desktop or Cursor).
Prerequisites
- A running instance of
tracker-server(usually onhttp://localhost:3000). - Python 3.11+ (if running locally) or Docker.
🚀 Running via Docker (Recommended)
When you deploy your MCP agent, you can hook it directly to the Docker image provided from the GitHub Container Registry.
Because tracker-server is running on your host machine locally, Docker needs to know how to reach it. The image defaults to http://host.docker.internal:3000 assuming you are on Mac/Windows or using Docker Desktop.
For Claude Desktop configuration (claude_desktop_config.json):
{
"mcpServers": {
"tracker-mcp": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"TRACKER_API_URL=http://host.docker.internal:3000",
"ghcr.io/makegorka/tracker-mcp:latest"
]
}
}
}
Note: Notice the
-iflag. Because MCP communicates over stdin/stdout (stdio), you must run the docker container interactively, but without a TTY (-t).
🛠Running Locally
If you are developing or simply prefer to run from source instead of Docker.
-
Setup the Virtual Environment:
cd tracker-mcp python -m venv venv source venv/bin/activate pip install -r requirements.txt -
Run the MCP Server manually:
# Make sure your tracker server is running! python server.pyNote: This will just block your terminal waiting for
stdioMCP JSON RPC commands. This is expected. -
Configure Claude Desktop (Local Mode):
{ "mcpServers": { "tracker-mcp": { "command": "/absolute/path/to/tracker-mcp/venv/bin/python", "args": [ "/absolute/path/to/tracker-mcp/server.py" ], "env": { "TRACKER_API_URL": "http://localhost:3000" } } } }
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