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GitHub PR Review Context

paarths-collab/github-pr-context-mcp
7registry active
Summary

Connects to GitHub's API to build a searchable index of your repository's PR history, including review comments, descriptions, and discussion threads. The v0.3.0 engine strips out built-in LLM logic and focuses purely on fast retrieval, letting your IDE agent (Cursor, Claude Desktop, Windsurf) handle the reasoning. You get tools like review_code_with_history to pull historical review patterns for code snippets, get_repo_rules_material to generate .cursorrules files grounded in actual team feedback, and semantic_search_reviews for querying past PR comments. Uses SQLite for progress tracking and async I/O with retries to handle large repository indexing. Reach for this when you want your agent to review code or generate features that actually match how your team works, backed by real repository history instead of generic best practices.

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Context.devContext.dev
Context.dev
Integrate web data into your AI product. One API to scrape website & brand data.
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Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
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Make money from your Skills
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On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
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🚀 GitHub PR Context Tools

Python Protocol Version Downloads

This repository provides two ways to leverage your GitHub PR history in your AI workflows. Choose the tool version that matches your architecture.


💎 GitHub PR Context (v0.3.0)

The Hardened Pure Context Retrieval Engine.

Designed for users who want maximum speed and reliability by offloading reasoning to their IDE agent (Cursor, Claude Desktop, Windsurf). It focuses exclusively on high-performance retrieval and data pruning.

✨ Key Features (v0.3.0):

  • Asynchronous Hardening: Built with tenacity retries and non-blocking I/O for heavy repository indexing.
  • Optimized for IDE Agents: Prunes redundant metadata and strips useless fields to save token space in your prompt.
  • Modern Infrastructure: Uses SQLite-backed cursors for progress tracking and global thread locks for data safety.
  • Pure Context: No built-in LLM dependencies; feeds raw, high-quality historical patterns directly to your primary agent.

🛠 Installation (Pre-release Source Install)

[!NOTE] v0.3.0 is currently in pre-release and is not yet live on PyPI. To use the hardened v0.3.0 engine today, you must install it from source:

  1. Fork this repository to your own account on GitHub.

  2. Clone and Checkout the release branch:

    git clone https://github.com/YOUR_USERNAME/github-pr-context-mcp.git
    cd github-pr-context-mcp
    git checkout v0.3.0-hardening
    
  3. Install via pipx (the whole command):

    pipx install -e .
    

    This links the github-pr-context-mcp command to your local repository folder.

  4. Verify Version:

    github-pr-context-mcp --help
    

🏛 Legacy Context Agent (v0.2.9)

The Integrated Reasoning Agent.

Designed for users who prefer the server to handle LLM inference internally. This version includes the legacy inference layer and supports direct configuration of OpenAI and Anthropic API keys.

✨ Key Features (v0.2.9):

  • Internal Inference: Can perform analysis and reviews directly using its own configured LLM provider.
  • All-in-One Logic: Includes the legacy inference/ module for direct model interactions.
  • Proven Stability: The original stable version of the tool.

🛠 Installation

pipx install github-pr-context-mcp==0.2.9

🛠️ Configuration (v0.3.0)

The v0.3.0 version only requires a GitHub Token.

Environment Variable: GITHUB_TOKEN=ghp_your_token_here


Overview

GitHub PR Review Context MCP gives AI assistants institutional review memory.

It fetches your repository's PR history (descriptions, review threads, comments), transforms them into searchable documents, and provides high-density Context Materials to your IDE agent.

Core Value

  • Historical Evidence: Tools return raw JSON "historical facts" (what reviewers cared about in the past).
  • Team Alignment: Your IDE agent uses this evidence to match your team's specific standards and architectural patterns.
  • High Performance: Optimized for fast retrieval and background indexing to prevent tool timeouts.

Key Capabilities

CapabilityWhat It Delivers
Historical RetrievalSemantic search across prior PR comments and review summaries.
Code Review MaterialRaw JSON context for the agent to perform grounded code reviews.
Rules MaterialHigh-density data for the agent to synthesize .cursorrules or CLAUDE.md.
Grounded GenerationContext materials for generating code that matches team style.
Namespace IsolationStrict isolation between users/teams using Gmail-based identity.

🚀 Quick Start

🚀 Recommended Installation (uvx / pipx)

# Run instantly
uvx github-pr-engine

# OR Install permanently
pipx install github-pr-engine

🛠️ Configuration

The only required configuration is your GitHub token.

Environment Variable: GITHUB_TOKEN=ghp_your_token_here


🧰 Tools Reference (Pure Context)

The server provides tools that return raw JSON context objects. The IDE agent then uses its own intelligence to process this data.

ToolActiontypical Use Case
ensure_repo_readyIndex a repo and ensure it's ready.Onboarding a new repository.
review_code_with_historyGet historical review material for a snippet."Review this code based on team history."
get_repo_rules_materialGet material to write .cursorrules."Write a rules file for this repo."
get_team_review_patternsGet raw patterns for summarization."What are the common review themes?"
generate_code_from_historyGet context for grounded generation."Write this feature in our team's style."
semantic_search_reviewsSearch past PR comments by meaning.Manual history lookup.
list_indexed_reposView all currently indexed repositories.Storage management.

📖 Documentation

  • 🏗️ Architecture & Pipeline — How the Pure Context engine works.
  • 🛠️ Quick Start Guide — Detailed setup instructions.
  • 🚀 Roadmap — Future development plans.

📣 Community & Feedback

  • Feedback: Please open an issue or start a discussion if you have ideas or encounter bugs.
  • Star ⭐: If this tool saves you time, give it a star!

⚖️ License

MIT

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CodeRabbit
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AI writes the code. CodeRabbit catches the slop.
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Categories
Developer ToolsSecurity & Pentesting
Registryactive
UpdatedMay 7, 2026
View on GitHub

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