GitHub PR Comments MCP Server

The GitHub PR Comments MCP server bridges the gap between AI assistants and real‑world code review data. By exposing a single tool, , it allows an AI client to pull the full context of a pull request’s discussion—including file paths, line ranges, and threaded replies—directly from GitHub. This capability is essential for developers who want their AI to reason about code changes, summarize feedback, or suggest fixes without leaving the assistant’s environment.

At its core, the server uses Octokit to authenticate with GitHub via a personal access token. Once authenticated, the tool queries the Pull Request API for all comments tied to a specific repository and pull request number. The response is returned in a structured JSON format that preserves the hierarchy of comments and replies, making it straightforward for downstream processing. Because the server is built on the Model Context Protocol, any Claude or other MCP‑compatible assistant can invoke it using a simple request, and the assistant receives a clean data payload that can be incorporated into prompts or further analysis.

Key features include:

• Structured output: Each comment includes metadata such as the file path, line numbers, author information, timestamps, and nested replies.
• Full context retrieval: By capturing the exact line ranges, the server enables precise code‑level insights and facilitates automated diff explanations.
• Ease of integration: The server runs over standard input/output (StdioServerTransport), allowing it to be launched as a standalone process or embedded in larger workflows like Cursor or Smithery.
• Security: Tokens are supplied via environment variables or command‑line arguments, keeping credentials out of the codebase.

Typical use cases involve AI‑powered code review assistants that can fetch and analyze comments in real time, generate summary reports for stakeholders, or trigger automated actions such as opening new issues when certain patterns are detected in review discussions. In continuous integration pipelines, the server can feed comment data into quality gates or compliance checks, ensuring that every change is evaluated against both human feedback and automated policies.

By delivering pull‑request comment data in a machine‑readable format, the GitHub PR Comments MCP server empowers developers to build richer, contextually aware AI tools that seamlessly integrate with their existing GitHub workflows.