LLM Responses MCP Server

# LLM Responses MCP Server A Model Context Protocol (MCP) server that allows multiple AI agents to share and read each other's responses to the same prompt. ## Overview This project implements an MCP server with two main tool calls: 1. `submit-response`: Allows an LLM to submit its response to a prompt 2. `get-responses`: Allows an LLM to retrieve all responses from other LLMs for a specific prompt This enables a scenario where multiple AI agents can be asked the same question by a user, and then using these tools, the agents can read and reflect on what other LLMs said to the same question. ## Installation ```bash # Install dependencies bun install ``` ## Development ```bash # Build the TypeScript code bun run build # Start the server in development mode bun run dev ``` ## Testing with MCP Inspector The project includes support for the [MCP Inspector](https://github.com/modelcontextprotocol/inspector), which is a tool for testing and debugging MCP servers. ```bash # Run the server with MCP Inspector bun run inspect ``` The `inspect` script uses `npx` to run the MCP Inspector, which will launch a web interface in your browser for interacting with your MCP server. This will allow you to: - Explore available tools and resources - Test tool calls with different parameters - View the server's responses - Debug your MCP server implementation ## Usage The server exposes two endpoints: - `/sse` - Server-Sent Events endpoint for MCP clients to connect - `/messages` - HTTP endpoint for MCP clients to send messages ### MCP Tools #### submit-response Submit an LLM's response to a prompt: ```typescript // Example tool call const result = await client.callTool({ name: 'submit-response', arguments: { llmId: 'claude-3-opus', prompt: 'What is the meaning of life?', response: 'The meaning of life is...' } }); ``` #### get-responses Retrieve all LLM responses, optionally filtered by prompt: ```typescript // Example tool call const result = await client.callTool({ name: 'get-responses', arguments: { prompt: 'What is the meaning of life?' // Optional } }); ``` ## License MIT ## Deployment to EC2 This project includes Docker configuration for easy deployment to EC2 or any other server environment. ### Prerequisites - An EC2 instance running Amazon Linux 2 or Ubuntu - Security group configured to allow inbound traffic on port 62886 - SSH access to the instance ### Deployment Steps 1. Clone the repository to your EC2 instance: ```bash git clone <your-repository-url> cd <repository-directory> ``` 2. Make the deployment script executable: ```bash chmod +x deploy.sh ``` 3. Run the deployment script: ```bash ./deploy.sh ``` The script will: - Install Docker and Docker Compose if they're not already installed - Build the Docker image - Start the container in detached mode - Display the public URL where your MCP server is accessible ### Manual Deployment If you prefer to deploy manually: 1. Build the Docker image: ```bash docker-compose build ``` 2. Start the container: ```bash docker-compose up -d ``` 3. Verify the container is running: ```bash docker-compose ps ``` ### Accessing the Server Once deployed, your MCP server will be accessible at: - `http://<ec2-public-ip>:62886/sse` - SSE endpoint - `http://<ec2-public-ip>:62886/messages` - Messages endpoint Make sure port 62886 is open in your EC2 security group!