hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Integrations

Used for running the server with commands like 'bun install', 'bun run build', and 'bun run dev'
Provides containerization and simplified deployment to EC2 or other server environments with included Docker configuration
Used for cloning the repository during deployment to EC2

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:

submit-response: Allows an LLM to submit its response to a prompt
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

# Install dependencies
bun install

Development

# 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, which is a tool for testing and debugging MCP servers.

# 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:

// 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:

// 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

Clone the repository to your EC2 instance:
git clone <your-repository-url> cd <repository-directory>
Make the deployment script executable:
chmod +x deploy.sh
Run the deployment script:
./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:

Build the Docker image:
docker-compose build
Start the container:
docker-compose up -d
Verify the container is running:
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!

This server cannot be installed

-

security - not tested

A

license - permissive license

-

quality - not tested

Enables multiple AI agents to share and read each other's responses to the same prompt, allowing them to reflect on what other LLMs said to the same question.

Appeared in Searches

How to Read and Write Excel Files