"GitHub repository operations: reading files, editing structure, and extracting data" matching MCP servers:

Enables natural-language analysis of GitHub repositories by exposing repository metadata, source code retrieval, search, and file reading as MCP tools, with answers grounded in the actual repository content.

Enables MCP-compatible LLM clients to retrieve repository metadata, list and read files, search code, and fetch README content through natural-language requests.

Enables users to profile public GitHub repositories via a single tool, checking for Spring/Java structure such as pom.xml, src/main/java, Java file counts, and Spring layer annotations to assess suitability for deep local analysis.

Enables large language models to explore, inspect, and analyze GitHub repositories through natural-language queries, using MCP tools to retrieve repository data, file structures, file contents, and code search results from the GitHub REST API.

Provides intelligent codebase analysis, dependency scanning, architecture detection, security vulnerability scanning, and automatic documentation generation for modern development teams.

MCP server exposing GitHub tools for issues, pull requests, and code browsing via the GitHub REST API. Designed for local LLM clients with flat arguments and streamable HTTP support.

Enables read access to GitHub source code, including code search, file contents, symbols, and Java-aware fragment extraction, with optional per-call repository overrides.

Provides tools for mapping and analyzing GitHub repositories. It allows users to set a GitHub Personal Access Token and retrieve detailed information about a specified repository, including its structure and summary statistics.

MCP server for fetching GitHub repository files and directory trees, supporting raw file content, unix-style directory trees, and enhanced trees with file sizes, depth limits, and filters.

Enables LLM hosts to perform scoped GitHub operations over stdio, including repository intelligence, pull request lifecycle and diff inspection, and commit history queries. It exposes 12 FastMCP tools backed by PyGithub for repository, PR, and commit workflows.

Enables self-hosted, read-only remote interaction with GitHub repositories, allowing listing repositories, searching code, and inspecting commits, pull requests, issues, and diffs via authenticated MCP clients.

Enables GitHub repository analysis including health score, comparison, commit heatmap, and trending MCP servers, with interactive widgets.

Enables AI agents to search repositories, inspect files and code, list issues and commits, check releases, and look up GitHub user profiles through the GitHub public REST API without authentication.

Stateless Streamable HTTP MCP server exposing 5 GitHub tools (user info, repository metadata, file contents, issue listing, and issue creation) with token-based authentication required only for tool calls.

Enables Claude to analyze any public GitHub repository through typed, focused tools that surface metadata, README content, file structure, detected tech stack, and Mermaid architecture diagrams. It lets users explore repo purpose, structure, and dependencies via natural language without manual browsing.

Provides four GitHub superpowers (repo-scorecard, compare-repos, commit-heatmap, trending-mcp) as MCP tools with React widgets for repository health analysis, comparison, commit activity visualization, and trending MCP servers.

Enables searching GitHub code with filters and reading arbitrary file contents from any repository via MCP.

Enables read-only browsing and querying of GitHub repositories, including listing repos, reading files, fetching READMEs, searching code, and viewing commit history via MCP.

A Model Context Protocol server that connects GitHub code to Claude.ai. This server utilizes the Pera1 service to extract code from GitHub repositories and provide better context to Claude.

An MCP server that turns any public GitHub repository into a PDF report containing a codebase summary, the most recent commits, and suggested improvements — generated locally by an Ollama model.