LLM Responses
About
Enables multiple LLMs to share and analyze each other's responses to the same prompt, facilitating collaborative problem-solving and multi-perspective analysis through TypeScript-based response submission and retrieval tools.
Details
- Author
- kstrikis
- Repository
- kstrikis/ephor-mcp
- GitHub stars
- 1
- License
- MIT License
- Categories
- Design, Workplace, Developer Tools, AI, Infrastructure, Communication
- Tags
- #mobile
Jump to
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
LLM ResponsesCommand (node, npx, python, etc.)npxArguments-
Argument 1
-y -
Argument 2
@highlight/mcp-server
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
-
Argument 1
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
The server exposes two endpoints:
- /sse - Server-Sent Events endpoint for MCP clients to connect
- /messages - HTTP endpoint for MCP clients to send messages
bun install
This project includes Docker configuration for easy deployment to EC2 or any other server environment.
1. Clone the repository to your EC2 instance:
git clone <your-repository-url>
cd <repository-directory>
2. Make the deployment script executable:
chmod +x deploy.sh
3. 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
If you prefer to deploy manually:
1. Build the Docker image:
docker-compose build
2. Start the container:
docker-compose up -d
3. Verify the container is running:
docker-compose ps
```
submit-response
Allows an LLM to submit its response to a prompt. Parameters: llmId (string), prompt (string), response (string)
get-responses
Allows an LLM to retrieve all responses from other LLMs for a specific prompt. Parameters: prompt (optional string)
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"llm responses": {
"env": {},
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}
}
}Linux
{
"env": [],
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}Macos
{
"env": [],
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}Windows
{
"env": [],
"args": [
"/c",
"npx",
"-y",
"@highlight/mcp-server"
],
"command": "cmd"
}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
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