Manages environment variables for the application, including the OpenAI API key and MCP server URL configuration.
Provides the server framework for the MCP implementation, exposing endpoints for tool discovery and JSON-RPC calls to interact with the to-do list application.
Provides version control for the MCP implementation, with instructions for cloning the repository during installation.
Hosts the source code repository for the to-do list MCP implementation, making it accessible for users to download and install.
Enables the CLI client to interact with OpenAI's LLMs to interpret natural language commands and execute appropriate function calls for managing tasks.
Serves as the programming language for both the server and client implementations, with support for Python 3.10+ required.
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
- Clone the repo:
- Create and activate a virtual environment:
- Install dependencies:
- Create a
.env
in the project root:
Directory Structure
Running the Server
- Visit http://127.0.0.1:8000/docs for interactive API docs.
Running the CLI
From the project root:
Type natural language commands at the prompt, for example:
The CLI will print tool invocations and LLM responses.
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
A minimal Model Context Protocol server implementation that provides tools for managing a to-do list, allowing users to create tasks, list them, and mark them as completed via JSON-RPC calls.
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