MCP Chat

MCP Chat is a command-line interface application that enables interactive chat with AI models through the OpenAI API. The application supports document retrieval, command-based prompts, and extensible tool integrations via the MCP (Model Control Protocol) architecture.

Prerequisites

• Python 3.10+
• OpenAI API Key

Setup

Step 1: Configure the environment variables

1. Create or edit the .env file in the project root and verify that the following variables are set correctly:

OPENAI_API_KEY=""     # Your OpenAI API key (do not commit real keys)
OPENAI_MODEL="gpt-4o-mini"  # Optional
USE_UV="0"            # Optional: set to 1 if you want main.py to start mcp_server via uv

Step 2: Install dependencies

Option 1: Setup with uv (Recommended)

uv is a fast Python package installer and resolver.

1. Install uv, if not already installed:

pip install uv

2. Create and activate a virtual environment:

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

uv pip install -e .

4. Run the project

uv run main.py

Option 2: Setup without uv

1. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

2. Install dependencies:

pip install -e .

3. Run the project

python main.py

Usage

Basic Interaction

Simply type your message and press Enter to chat with the model.

Document Retrieval

Use the @ symbol followed by a document ID to include document content in your query:

> Tell me about @deposition.md

Commands

Use the / prefix to execute commands defined in the MCP server:

> /summarize deposition.md

Commands will auto-complete when you press Tab.

Development

Adding New Documents

Edit the mcp_server.py file to add new documents to the docs dictionary.

Implementing MCP Features

To fully implement the MCP features:

1. Complete the TODOs in mcp_server.py
2. Implement the missing functionality in mcp_client.py

Linting and Typing Check

There are no lint or type checks implemented.