MCP Container

root-signals-mcp

Root Signals MCP is a server designed for AI assistants and automation tools, implementing the Model Context Protocol (MCP). It provides evaluators as tools for measuring and controlling LLM (Large Language Model) automations. Key features include integration with Hugging Face and comprehensive documentation.

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Installation
Difficulty
Intermediate
Estimated Time
10-20 minutes
Requirements
Docker: Latest version

Installation

Installation

Prerequisites

Please specify required software and versions:
Docker: Latest version

Installation Steps

1. Install Docker

If Docker is not installed, please install it from the official website.

2. Run the MCP Server

Execute the following command to start the MCP server: 
bash
docker run -e ROOT_SIGNALS_API_KEY= -p 0.0.0.0:9090:9090 --name=rs-mcp -d ghcr.io/root-signals/root-signals-mcp:latest

3. Check Logs

To confirm that the server has started correctly, check the logs with the following command: 
bash
docker logs rs-mcp

Troubleshooting

Common Issues

Issue: Server won't start Solution: Verify that the API key is correct and review Docker settings.

Configuration

Configuration

Basic Configuration

Environment Variable Setup

Before running the MCP server, you need to set the API key as an environment variable: 
bash
export ROOT_SIGNALS_API_KEY="your-api-key"

Advanced Configuration

Security Settings

Store the API key in a secure location and manage it as an environment variable.
If changing the server port number, specify it with the -p option.

Configuration Example

bash

Example Docker command


docker run -e ROOT_SIGNALS_API_KEY="your-api-key" -p 0.0.0.0:9090:9090 --name=rs-mcp -d ghcr.io/root-signals/root-signals-mcp:latest

Examples

Examples

Basic Usage

Here are basic usage examples for the MCP server:

Verify MCP Server Startup

bash

Check server startup

docker logs rs-mcp

List Evaluators

bash
curl -X GET http://localhost:9090/mcp/list_evaluators

Run Evaluation

bash
curl -X POST http://localhost:9090/mcp/run_evaluation -H "Content-Type: application/json" -d '{"evaluator_id": "your_evaluator_id"}'

Use Cases

Evaluating responses generated by AI assistants for user queries.
Automated evaluations to check code policy adherence by developers.
Conducting evaluations to measure AI model performance and identify areas for improvement.
Utilizing evaluation tools to compare response quality among different AI agents.
Author Information
root-signals
root-signals
GitHub

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Total Contributions

Tags
agentic-aievalsllm-as-a-judgemcpmodel-context-protocolpydantic-ai
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