TaskFlow MCP

A small Model Context Protocol server that gives an AI coding agent (Claude Code, Claude Desktop, or any other MCP client) direct read/write access to a task tracker, backed by SQLite.

I built this after using Claude Code to run a job-application pipeline and wanting the same thing for my own projects: a lightweight board an agent can query and update mid-conversation, without a browser tab or a hosted project-management tool in the way.

What it exposes

Tools

Resources

Related MCP server: MCP Project Manager

Install

git clone https://github.com/SyedFrazAli/taskflow-mcp.git
cd taskflow-mcp
pip install -e ".[dev]"

Run the tests

pytest

17 tests cover the SQLite layer (tests/test_db.py) and the MCP tool/resource handlers (tests/test_server.py), including validation errors and the empty-board edge case.

Use it from Claude Desktop or Claude Code

Add it to your MCP client config (for Claude Desktop, that's claude_desktop_config.json; Claude Code uses the same shape):

{
  "mcpServers": {
    "taskflow": {
      "command": "python",
      "args": ["-m", "taskflow_mcp.server"],
      "env": {
        "TASKFLOW_DB_PATH": "/absolute/path/to/taskflow.db"
      }
    }
  }
}

Restart the client, and it can then call add_task, list_tasks, update_task_status, and delete_task, or read the tasks://board resource to see the current state as markdown.

Design notes

• The SQLite layer (db.py) has no dependency on the mcp package, so it is unit tested directly, without a client/server round trip.

• The server layer (server.py) is a thin adapter: it validates nothing itself and instead lets db.py raise InvalidTaskFieldError / TaskNotFoundError, which it turns into an Error: ... text response rather than letting the MCP transport see an unhandled exception.

• TaskStore(":memory:") is used throughout the test suite so tests never touch disk or leave a taskflow.db file behind.

License

MIT — see LICENSE.