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mcp-tasks-server

by kanaparthikiran
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mcp-tasks-server

A small Model Context Protocol server that exposes a personal task tracker to any MCP-compatible client (Claude Desktop, Claude Code, custom agents). Tasks live in a local SQLite database; no cloud, no surprises.

This is a reference implementation of an MCP server that takes real write-actions on persistent state — the kind of integration you build between an agentic AI platform and an enterprise system, scaled down to a laptop.

Tools exposed

Tool

Effect on state

add_task

Insert a new task

list_tasks

Read; optional status filter

complete_task

Update status='done'

delete_task

Hard-delete a task

summarize_by_status

Read; aggregate counts

Each tool has a JSON schema declared in code, validated on every call.

Related MCP server: TaskMCP

Quick start

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python server.py

Wire into Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "tasks": {
      "command": "python",
      "args": ["/absolute/path/to/server.py"]
    }
  }
}

Then restart Claude Desktop. The tools appear in the MCP picker.

Sandbox / safety notes

  • Database file lives at ~/.local/share/mcp-tasks/tasks.db by default; override with MCP_TASKS_DB. The path is normalized and confined to the user's home directory.

  • delete_task requires the task ID to be an integer; no SQL is built from string concatenation. All queries use parameterized statements.

  • The server has no network listeners — communication is over stdio per the MCP spec.

Sample tool calls

The full request/response shapes for every tool are in examples/tool-call-examples.md. Highlights:

Tool

Example call

Example result

add_task

{"title": "Review RFC", "notes": "..."}

{"id": 1, "title": "...", "status": "open"}

list_tasks

{"status": "open"}

{"tasks": [...]}

complete_task

{"id": 1}

{"id": 1, "status": "done"}

summarize_by_status

{}

{"counts": {"open": 2, "done": 1}}

Design notes

  • Why SQLite? Embedded, transactional, present on every Python install. A real enterprise integration would target a service API; the contract on the agent side is identical.

  • Why explicit JSON schemas? Some MCP clients display the schemas to the user before approving a tool call. Putting them inline in the server makes tool surfaces self-documenting and gives the LLM unambiguous parameter contracts.

  • Why no auth? Personal/local server. For an enterprise MCP server you'd add OIDC token verification at the tool boundary and scope each tool to the calling principal's permissions.

License

MIT — see LICENSE.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

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