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- ephor-mcp
Ephor Mcp
What is Ephor Mcp
ephor-mcp is a Model Context Protocol (MCP) server designed to facilitate communication between multiple AI agents, allowing them to share and read each other’s responses to the same prompt.
Use cases
Use cases for ephor-mcp include collaborative question-answering systems, AI training environments where agents learn from each other, and applications that require diverse perspectives on a single prompt.
How to use
To use ephor-mcp, install the dependencies using ‘bun install’, build the TypeScript code with ‘bun run build’, and start the server in development mode using ‘bun run dev’. You can interact with the server through its endpoints for submitting and retrieving responses.
Key features
Key features of ephor-mcp include the ability to submit responses from different LLMs to a prompt and retrieve all responses for analysis. It also supports the MCP Inspector for testing and debugging.
Where to use
ephor-mcp can be used in fields such as AI research, collaborative AI development, and any scenario where multiple AI agents need to share insights or responses to the same queries.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Ephor Mcp
ephor-mcp is a Model Context Protocol (MCP) server designed to facilitate communication between multiple AI agents, allowing them to share and read each other’s responses to the same prompt.
Use cases
Use cases for ephor-mcp include collaborative question-answering systems, AI training environments where agents learn from each other, and applications that require diverse perspectives on a single prompt.
How to use
To use ephor-mcp, install the dependencies using ‘bun install’, build the TypeScript code with ‘bun run build’, and start the server in development mode using ‘bun run dev’. You can interact with the server through its endpoints for submitting and retrieving responses.
Key features
Key features of ephor-mcp include the ability to submit responses from different LLMs to a prompt and retrieve all responses for analysis. It also supports the MCP Inspector for testing and debugging.
Where to use
ephor-mcp can be used in fields such as AI research, collaborative AI development, and any scenario where multiple AI agents need to share insights or responses to the same queries.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
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 promptget-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 endpointhttp://<ec2-public-ip>:62886/messages- Messages endpoint
Make sure port 62886 is open in your EC2 security group!
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.










