py-mcp-sse

# This project was built on 2025-03-30 and is a sample implementation of MCP Client and MCP Server (Python version) ## It is based on https://github.com/sidharthrajaram/mcp-sse for optimization and implementation [The original project is based on the original discussion of (https://github.com/modelcontextprotocol/python-sdk/issues/145), you can download the original project for more information]. ## The original version was implemented using ANTHROPIC LLM, and this version is adjusted to use OpenRouter.ai's LLM transfer call platform. # SSE-based [MCP](https://modelcontextprotocol.io/introduction) Server and Client ## `mcp_server.py` is an SSE-based MCP server that provides a book search tool based on the Gutenberg Project API. Adapted from the [example STDIO server implementation](https://modelcontextprotocol.io/quickstart/server) in the MCP documentation. ## `client.py` is an MCP client that can connect to and use tools provided by an SSE-based MCP server. Adapted from the [example STDIO client implementation](https://modelcontextprotocol.io/quickstart/client) in the MCP documentation. ## System Requirements - Python 3.13 or higher - uv package manager (recommended) or pip ## Installation Steps 1. **Clone the project** ```bash git clone [your-repository-url] cd jay-py-mcp-sse ``` 2. **Environment Configuration** Using uv (recommended): ```bash # Install uv (if not installed) curl -LsSf https://astral.sh/uv/install.sh | sh # Create a virtual environment and install dependencies uv venv uv pip install . ``` Or use traditional pip: ```bash # Create a virtual environment python -m venv .venv source .venv/bin/activate # Linux/MacOS # Or .venv\Scripts\activate # Windows # Install dependencies pip install . ``` 3. **Configure Environment Variables** Copy the environment variable sample file and fill in the necessary information: ```bash cp .env.sample .env ``` Edit the `.env` file and enter your OpenRouter API key: ```plaintext OPENROUTER_API_KEY=your_api_key_here ``` ## Running the Service 1. **Start the server** ```bash uv run mcp_server.py # Or specify the host and port uv run mcp_server.py --host 127.0.0.1 --port 8080 ``` 2. **Start the client** ```bash uv run client.py http://localhost:8080/sse ``` ## Example Output ``` Initialized SSE client... Listing tools... Connected to server, available tools: ['search_gutenberg_books'] AI assistant started! Enter your question or type 'quit' to exit. User: Please recommend some works by James Joyce AI: Let me help you search for works by James Joyce... [Calling tool search_gutenberg_books, parameters: {"search_terms": ["James Joyce"]}] Found the following works: - Ulysses - Dubliners - A Portrait of the Artist as a Young Man ... ``` ## Project Structure ``` . ├── mcp_server.py # SSE server implementation ├── client.py # MCP client implementation ├── .env.sample # Environment variable sample ├── pyproject.toml # Project dependencies and configuration management └── README.md # Project documentation ``` ## Why Choose This Approach? This implementation means that the MCP server can now be a running process, and the proxy (client) can connect, use, and disconnect at any time. In other words, the SSE-based server and client can be decoupled processes (and can even run on decoupled nodes). This is different from the STDIO-based model, where the client itself starts the server as a child process. This approach is more suitable for "cloud-native" use cases. ## Troubleshooting If you encounter problems, please check: 1. Whether the Python version meets the requirements (>=3.13) 2. Whether the environment variables are configured correctly 3. Whether all dependencies are installed correctly 4. Whether port 8080 is occupied (if it is occupied, you can use the --port parameter to specify another port) ## Get Help If you encounter problems, please: 1. Check the project's GitHub Issues page 2. Provide complete error information and runtime environment information 3. Describe the steps to reproduce the problem