README.md•3.48 kB
# 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!