MCP Chat

# MCP Chat MCP Chat is a command-line interface application. The application supports document retrieval, command-based prompts, and extensible tool integrations via the MCP (Model Control Protocol) architecture. ## Prerequisites - Python 3.9+ - Any Chat Completions LLM API Key and Provider (i.e: Gemini) ## 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: ``` LLM_API_KEY="" # Enter your GEMINI API secret key LLM_CHAT_COMPLETION_URL="https://generativelanguage.googleapis.com/v1beta/openai/" LLM_MODEL="gemini-2.0-flash" ``` ### Step 2: Install dependencies [uv](https://github.com/astral-sh/uv) is a fast Python package installer and resolver. 1. Install uv, if not already installed: ```bash pip install uv ``` 2. Create and activate a virtual environment: ```bash uv venv source .venv/bin/activate # On Windows: .venv\Scripts\activate ``` 3. Install dependencies: ```bash uv sync ``` 4. Start MCP Server: ```bash uv run uvicorn mcp_server:mcp_app --reload ``` 5. Run the project with ChatAgent in CLI ```bash uv run main.py ``` 6. Optionally start inspector ```bash npx @modelcontextprotocol/inspector ``` ## 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.