GitHub Stars

Integrates with GitHub's GraphQL API to analyze user starred repositories, track starring patterns over time, and extract technology adoption insights with timeline analysis and language distribution breakdowns.

This MCP server provides GitHub stars analysis and timeline tracking capabilities through the GitHub GraphQL API, enabling AI assistants to retrieve user starred repositories, analyze starring patterns over time, and extract technology adoption insights. Built using FastMCP 2.2.0+ with Python 3.11+, it offers tools for fetching starred repositories with pagination support, timeline analysis with monthly activity breakdowns, language distribution analysis, and repository ranking by popularity. The implementation features async/await patterns for performance, TTL caching for API response optimization, structured logging with structlog, robust error handling for GitHub API rate limits and authentication failures, and comprehensive data models using Pydantic for validation, making it valuable for developer research workflows, technology trend analysis, and building AI assistants that need programmatic access to GitHub starring behavior without manual repository browsing.