mcp-client

# MCP Client A Python client for connecting to Machine-to-Machine Communication Protocol (MCP) servers. This client allows you to interact with both stdio and SSE MCP servers, enabling LLM agents to use tools through a standardized protocol. ## Features - Connect to stdio MCP servers (Python and JavaScript) - Connect to SSE MCP servers - Interactive chat interface with multiple LLM options: - Anthropic Claude 3.5 Sonnet (default) - OpenAI GPT-4o - Google Gemini 2.0 Flash - Tool calling with automatic result processing - Conversation history management with refresh capability - Detailed logging for debugging and monitoring ## Prerequisites - Python 3.8+ - API keys set as environment variables: - `ANTHROPIC_API_KEY` for Anthropic Claude - `OPENAI_API_KEY` for OpenAI GPT models - `GOOGLE_API_KEY` for Google Gemini ## Installation 1. Clone the repository 2. Install the required packages: ```bash pip install mcp-protocol-client anthropic openai google-genai python-dotenv ``` 3. Create a `.env` file with your API keys: ``` ANTHROPIC_API_KEY=your_anthropic_api_key OPENAI_API_KEY=your_openai_api_key GOOGLE_API_KEY=your_google_api_key ``` 4. Create a `logs` directory to store client logs: ```bash mkdir logs ``` ## Usage ### Basic Usage ```bash python client.py <server_script_path_or_url> [llm_provider] ``` Where: - `<server_script_path_or_url>` is either a path to an MCP server script or URL to an SSE MCP server - `[llm_provider]` is optional and can be one of: `anthropic` (default), `openai`, or `gemini` ### Examples 1. Connect to a Python MCP server using Anthropic Claude (default): ```bash python client.py ./weather.py ``` 2. Connect to a JavaScript npm MCP server using OpenAI: ```bash python client.py @playwright/mcp@latest openai ``` 3. Connect to an SSE MCP server using Google Gemini: ```bash python client.py http://localhost:8000/sse gemini ``` ### Interactive Chat Commands - Type your queries to interact with the LLM and tools - Type `refresh` to clear conversation history - Type `quit` to exit the application ## Development The client includes VS Code launch configurations for various setups, making it easy to debug and test with different servers and LLM providers. ## How It Works The MCP Client: 1. Connects to an MCP server (either stdio or SSE) 2. Lists available tools from the server 3. Processes user queries by: - Sending the query to the selected LLM with available tools - Detecting and executing tool calls when the LLM requests them - Sending tool results back to the LLM for processing - Providing the final response to the user 4. Maintains conversation history for context ## Logging Logs are stored in `logs/mcp_client.log` and are also displayed in the console. The logging level can be adjusted in the client.py file.