ghcontext

Overview

The ghcontext MCP server solves a common pain point for developers building AI‑powered tooling: the lag between real repository changes and the knowledge that a large language model (LLM) has about those repos. When an LLM’s internal knowledge base is static, it can no longer answer questions about recent commits, new API endpoints, or updated documentation. ghcontext bridges that gap by exposing a lightweight, real‑time interface to GitHub’s API through the Model Context Protocol (MCP). Developers can therefore give their assistants up‑to‑date, repository‑specific information without compromising the privacy of private projects or incurring unnecessary API costs.

At its core, ghcontext acts as a translation layer. It accepts MCP requests from any compliant LLM (Claude, GPT‑4o, etc.) and translates them into GitHub GraphQL or REST calls. The server then returns structured data—repository metadata, README text, API documentation snippets, file contents, and search results—in a format the LLM can ingest. Because all interactions are performed through MCP, the same set of tools can be reused across different models and workflows, keeping integration simple and consistent.

Key capabilities include:

• Real‑time README & API docs extraction – The server parses Markdown and code comments to surface the latest public documentation.
• Repository structure mapping – A lightweight graph of directories and files lets the LLM reason about architecture or locate specific modules.
• Targeted file search – Using filename patterns or content queries, the assistant can pull in code snippets or configuration files on demand.
• Repository discovery – Search across GitHub for projects that match criteria such as language, stars, or topic tags.
• Caching with freshness guarantees – An intelligent cache reduces redundant GitHub calls while ensuring data is refreshed when the underlying repo changes.

In practice, ghcontext empowers a range of use cases:

• Code review assistants that pull the latest function signatures and usage examples.
• Documentation generators that automatically include up‑to‑date API references from a repo’s README.
• Onboarding bots that walk new contributors through the current project structure and coding standards.
• Debugging helpers that fetch recent commits or configuration files to diagnose failures.

Integration is straightforward: an LLM simply connects to the server’s MCP endpoint () and invokes tools such as or . The server handles authentication via a GitHub token supplied at launch, respecting the minimal scopes required for read‑only access. Because ghcontext adheres to MCP’s resource and tool definitions, developers can embed it into existing AI pipelines—whether orchestrated by a custom middleware or a managed service—without rewriting prompts or changing model architectures. The result is an assistant that can answer “What are the available methods in axios for handling request interceptors?” with a freshly fetched, repository‑accurate response rather than stale or generic knowledge.