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TODO MCP CLI & Server

This repository contains a minimal Model Context Protocol (MCP) implementation for a to-do list application, including:

  • FastAPI server (server/): exposes a /tools endpoint for tool discovery and an /rpc endpoint for JSON-RPC calls to perform operations on tasks.

  • CLI client (client/cli.py): a Python command-line interface that interacts with an LLM (via OpenAI) and the MCP server to create, list, and complete tasks using function calls.


Features

  • Add tasks with title, content, and optional due date

  • List all tasks

  • Mark tasks as completed

  • Server-side task ID generation

  • JSON-RPC 2.0 compliance for tool invocation


Prerequisites

  • Python 3.10+

  • pipenv or venv for virtual environments

  • An OpenAI API key


Installation

  1. Clone the repo:

    git clone https://github.com/oseni99/todo-mcp cd todo-mcp
  2. Create and activate a virtual environment:

    python3 -m venv .venv source .venv/bin/activate
  3. Install dependencies:

    pip install -r requirements.txt
  4. Create a .env in the project root:

    OPENAI_API_KEY=sk-... MCP_SERVER=http://127.0.0.1:8000

Directory Structure

todoMCP/ ├── client/ # CLI client code │ └── cli.py # Main entrypoint for the MCP-CLI ├── server/ # FastAPI server code │ ├── handlers.py # Business logic for add, list, complete │ ├── tools.py # JSON-Schema tool manifest │ └── main.py # FastAPI app with /tools and /rpc ├── .env # Environment variables (not committed) ├── requirements.txt # Python dependencies └── README.md # This file

Running the Server

fastapi dev server/main.py

Running the CLI

From the project root:

python -m client.cli

Type natural language commands at the prompt, for example:

> Create a task titled "Write blog post" with content "Outline first draft" due 2025-05-20 > List my tasks > Mark the first task as done > Thanks! > exit

The CLI will print tool invocations and LLM responses.


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security - not tested
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license - not found
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