mcp-client-postgres

# MCP Simple Chatbot This example demonstrates how to integrate the Model Context Protocol (MCP) into a simple CLI chatbot. The implementation showcases MCP's flexibility by supporting multiple tools through MCP servers and is compatible with any LLM provider that follows OpenAI API standards. ## Requirements - Python 3.10 - `python-dotenv` - `requests` - `mcp` - `uvicorn` ## Installation 1. **Install the dependencies:** ```bash pip install -r requirements.txt ``` 2. **Set up environment variables:** Create a `.env` file in the root directory and add your API key: ```plaintext LLM_API_KEY=your_api_key_here ``` 3. **Configure servers:** The `servers_config.json` follows the same structure as Claude Desktop, allowing for easy integration of multiple servers. Here's an example: ```json { "mcpServers": { "sqlite": { "command": "uvx", "args": ["mcp-server-sqlite", "--db-path", "./test.db"] }, "puppeteer": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-puppeteer"] } } } ``` Environment variables are supported as well. Pass them as you would with the Claude Desktop App. Example: ```json { "mcpServers": { "postgres": { "command": "npx", "args": [ "-y", "@modelcontextprotocol/server-postgres", "postgresql://postgres:postgres@localhost:5432/ssd" ] } } } ``` ## Usage 1. **Run the client:** ```bash python main.py ``` 2. **Interact with the assistant:** The assistant will automatically detect available tools and can respond to queries based on the tools provided by the configured servers. 3. **Exit the session:** Type `quit` or `exit` to end the session. ## Architecture - **Tool Discovery**: Tools are automatically discovered from configured servers. - **System Prompt**: Tools are dynamically included in the system prompt, allowing the LLM to understand available capabilities. - **Server Integration**: Supports any MCP-compatible server, tested with various server implementations including Uvicorn and Node.js. ### Class Structure - **Configuration**: Manages environment variables and server configurations - **Server**: Handles MCP server initialization, tool discovery, and execution - **Tool**: Represents individual tools with their properties and formatting - **LLMClient**: Manages communication with the LLM provider - **ChatSession**: Orchestrates the interaction between user, LLM, and tools ### Logic Flow 1. **Tool Integration**: - Tools are dynamically discovered from MCP servers - Tool descriptions are automatically included in system prompt - Tool execution is handled through standardized MCP protocol 2. **Runtime Flow**: - User input is received - Input is sent to LLM with context of available tools - LLM response is parsed: - If it's a tool call → execute tool and return result - If it's a direct response → return to user - Tool results are sent back to LLM for interpretation - Final response is presented to user