tomwey2/task-tracker-mcp-server-python-fastmcp
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This document provides a comprehensive summary of the MCP Server for a Task Tracker application.
Task Tracker MCP Server
Overview
This project is a Model Context Protocol (MCP) server that acts as a bridge to a task-tracking application backend. It handles authentication against the backend and exposes a set of tools and resources that an MCP client (like a large language model or an AI assistant) can use to interact with the task management system.
The server is built using Python and the fastmcp library. On startup, it authenticates with the backend using credentials provided via a .env file and maintains an authenticated session for all subsequent API calls.
Available Tools
The following tools, resources, and prompts are exposed by this MCP server:
Tools
get_tasks(project_id: Optional[int], assigned_user_id: Optional[int]) -> Dict- Retrieves tasks from the backend.
- Can be filtered by
project_id. - If
assigned_user_idis not provided, it defaults to fetching tasks assigned to the currently authenticated agent.
Installation and Setup
-
Clone the repository:
git clone <repository-url> cd task-tracker-mcp-server-python-fastmcp -
Create and activate a virtual environment using
uv:uv venv source .venv/bin/activate -
Install dependencies using
uv: The project's dependencies are defined inpyproject.tomland locked inuv.lock. Useuv syncto install them.uv sync -
Create a
.envfile: This server loads configuration from a.envfile in the project root. Create this file and add the necessary credentials:BACKEND_URL="http://your-backend-api-url.com" TASKAPP_USER="your-username" TASKAPP_PASSWORD="your-password"
Testing with MCP Inspector
Once the server is configured, you can test it locally.
-
Run the server:
uv run mcp run main.pyOn startup, the server will attempt to log in to the backend. You should see log messages indicating success or failure.
-
Use the MCP Inspector: In a separate terminal (with the virtual environment activated), use the
mcpCLI to inspect the running server and test its tools.uv run mcp dev main.pyThis will open an interactive inspector where you can see available tools and call them. For example, to call the
get_taskstool:call get_tasks --params '{"project_id": 1}'
Configuration in Zed
To use this MCP server with the Zed editor, you need to configure it in your settings.json file.
- Open Zed and go to
File > Settings(orCmd + ,). - Click "Open JSON" to edit the
settings.jsonfile. - Add the following configuration to the
context_serverslist.
{
"context_servers": {
"taskapp-mcp-server": {
"source": "custom",
"command": "uv",
"args": [
"run",
"--with",
"mcp,httpx",
"mcp",
"run",
"main.py"
],
"env": {
"BACKEND_URL": "your_url_to_the_backend",
"TASKAPP_USER": "your_username",
"TASKAPP_PASSWORD": "your_password"
}
},
After saving the settings, Zed will be able to communicate with your MCP server.