OSS Metrics MCP Server

A Model Context Protocol (MCP) server that analyzes GitHub repositories for contributor activity, sentiment analysis, and project health metrics.

Features

• Complete contributor analysis - Activity trends and patterns
• Email domain classification - Company/personal/academic contributor categorization
• Sentiment analysis - Comment and interaction sentiment analysis
• Repository statistics - Response times, close rates, commit frequency
• Risk assessment - Project sustainability and health metrics
• Custom domain filtering - Configure company domains for analysis

Tools

analyze_repository_contributors

Analyze a single GitHub repository for detailed contributor activity and metrics.

Parameters:

• repository_url (required): GitHub repository URL to analyze
• analysis_days (optional): Number of days to look back (1-365, default: 365)
• include_sentiment (optional): Whether to include sentiment analysis (default: false, slower but more detailed)

analyze_multiple_repositories

Analyze and compare multiple GitHub repositories.

Setup

Prerequisites

1. Python Dependencies:

   pip install -r requirements.txt

2. GitHub Token: Create a .env file with your GitHub token:

   cp .env.example .env
   # Edit .env and add your GitHub token

3. Quick Setup: Use the setup script:

   ./setup.sh

Running the MCP Server

# Activate environment
./activate.sh

# Run MCP server
python3 mcp_server.py

1. Python 3.9+
2. GitHub Personal Access Token - Create at GitHub Settings > Developer settings > Personal access tokens

Installation

# 1. Clone the repository
git clone https://github.com/emmairwin/mcp-oss-metrics.git
cd mcp-oss-metrics

# 2. Install dependencies
pip install -r requirements.txt

# 3. Set up environment variables
cp .env.example .env
# Edit .env and add your GitHub token

# 4. Test the server
python test_server.py

Note: The MCP server is designed to be used with an MCP client (like Claude Desktop). Running python mcp_server.py directly will start the server and wait for MCP client connections via stdio.

Configure with Claude Desktop

Add to your Claude Desktop MCP configuration:

{
  "mcpServers": {
    "oss-metrics": {
      "command": "python",
      "args": ["/path/to/mcp-oss-metrics/mcp_server.py"],
      "env": {
        "GITHUB_TOKEN": "your_github_token_here"
      }
    }
  }
}

Windows users: Use the full path to your Python executable:

{
  "mcpServers": {
    "oss-metrics": {
      "command": "C:\\Python313\\python.exe",
      "args": ["C:\\path\\to\\mcp-oss-metrics\\mcp_server.py"],
      "env": {
        "GITHUB_TOKEN": "your_github_token_here"
      }
    }
  }
}

Usage

Ask Claude to analyze repositories:

• "Analyze the contributors of https://github.com/microsoft/vscode for the last 90 days"
• "Analyze https://github.com/facebook/react with sentiment analysis included"
• "Compare contributor activity between https://github.com/facebook/react and https://github.com/vuejs/vue"
• "What are the sustainability risks for this repository?"
• "Has Emma Irwin contributed to x repository in the last 100 days?"
• "Is Emma's contribution activity increasing or decreasing?"

Note: Sentiment analysis is optional and significantly slower. Only request it when you need detailed sentiment insights about contributor interactions.

Environment Variables

• GITHUB_TOKEN - Required: GitHub Personal Access Token for API access
• GITHUB_API_URL - Optional: Custom GitHub API URL (defaults to https://api.github.com)

Analysis Output

The server provides comprehensive repository analysis including:

• Contributor Activity: Detailed metrics for each contributor
• Email Domain Analysis: Company vs personal contributor classification
• Sentiment Analysis: Comment and interaction sentiment scoring
• Risk Assessment: Project sustainability and health metrics
• Temporal Trends: Activity patterns over timetext