Overview

The GitHub Star Count MCP Server provides a lightweight, purpose‑built interface for AI assistants to retrieve star counts from any public GitHub repository. By exposing a single, well‑defined resource via the Model Context Protocol (MCP), Claude can query repository popularity metrics without leaving its conversational context. This eliminates the need for developers to manually call GitHub’s REST API, parse JSON responses, or handle authentication logic in their own code.

What Problem Does It Solve?

Developers building AI‑powered tools often need quick, reliable access to external data sources. Fetching GitHub star counts is a common requirement for analytics dashboards, dependency monitoring, or recommendation engines. Traditionally, this would involve writing HTTP requests, managing OAuth tokens, and parsing responses—all boilerplate that distracts from core application logic. The MCP server abstracts these details, letting AI assistants ask a natural language question and receive the star count directly.

How It Works for Developers

The server runs as a standalone process and registers itself with Claude Desktop through a simple JSON configuration. Once registered, any prompt that references the MCP can trigger a call to the server. The MCP server internally authenticates with GitHub using a personal access token supplied via an environment variable, queries the repository’s metadata endpoint, and returns only the star count. Because MCP focuses on resources rather than full APIs, the client side remains minimal: a single prompt line is enough to obtain the desired data.

Key Features and Capabilities

• Single‑resource focus: Only star counts are exposed, keeping the protocol surface small and easy to reason about.
• Secure authentication: The server uses a GitHub personal access token, keeping credentials out of client code.
• Zero‑configuration integration: Adding the MCP to Claude Desktop requires just a JSON snippet and a shell script path.
• Extensibility: The project structure (e.g., ) makes it straightforward to add more GitHub metrics—issues, forks, or commit activity—in future iterations.

Real‑World Use Cases

• Developer dashboards: An AI assistant can answer questions like “How many stars does have?” and embed the result in a status report.
• Project triage: Teams can quickly compare repository popularity when deciding which libraries to adopt or maintain.
• Educational tools: Instructors can demonstrate how MCP servers bridge conversational AI with external data sources, using star counts as a concrete example.

Unique Advantages

Unlike generic HTTP clients or custom SDK wrappers, this MCP server offers a domain‑specific integration that is both lightweight and secure. By limiting the exposed functionality to star counts, it reduces attack surface and simplifies permission management. Its tight coupling with Claude Desktop means developers can leverage the same workflow they use for other MCP servers, creating a consistent developer experience across disparate data sources.