github-pr-context-mcp 0.2.5

GitHub PR Review Context MCP Server

GitHub PR Review Context MCP

Production-grade context layer for AI code review, grounded in your repository's real pull request history.

Tracking unique users across uvx, pipx, and local sources. (Render hosting upcoming)

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Overview

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

Instead of generic feedback, reviews are informed by historical reviewer comments, recurring quality patterns, and repository-specific standards from your own PR history.

Core Value

• Improves review consistency across teams and repositories.
• Reduces repeated reviewer feedback on known issues.
• Integrates with any MCP-compatible client and multiple LLM providers.

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🛠️ Usage Modes: Solo vs. Team

This MCP server is built to scale from a single machine to an entire engineering organization.

👤 Solo Developer (Local Mode)

Best for: Privacy, local-first control, and zero hosting costs.

• How it works: Run via uvx, pipx, or a local git clone.
• Storage: ChromaDB stays on your local machine.
• Security: Your GitHub Token and LLM keys never leave your device.
• Setup: See Quick Start.

🤝 Team Collaboration (Hosted Mode - UPCOMING)

Best for: Scaling team-wide PR standards and centralized infra.

• How it works: One deployment on Render (Coming Soon) shared by the whole team.
• Isolation: Strict Gmail-based namespace isolation (driven by SQLite). User A's indexed data is mathematically invisible to User B.
• Economics: Pooled LLM credits and a single shared indexing server.
• Setup: See Deployment Guide.

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🌟 Zero-Friction Setup (Upcoming)

If your team has Hosted this MCP on Render, you do NOT need to git clone or install anything. You just drop a snippet into your IDE:

"github-pr-context": {
  "type": "sse",
  "url": "https://YOUR-RENDER-URL.onrender.com/mcp",
  "headers": {
    "Authorization": "Bearer YOUR_TOKEN"
  }
}

That's it. If your IDE supports native MCP SSE connections, you are immediately connected to the secure Render deployment. No setup friction, no tools required.

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Key Capabilities

Capability	What It Delivers
Historical review retrieval	Semantic search across prior PR comments and review summaries
Context-aware AI review	Feedback grounded in repository-specific review behavior
Grounded code generation	Generate new code based on past commits, comments, and style
Team rules generation	Auto-generate .cursorrules / CLAUDE.md from repo history
Smart repository readiness	Auto-detect indexed state and index on demand
Flexible storage modes	Permanent (disk) and temporary (in-memory) indexing options
Portable inference layer	Switch LLM providers using environment configuration only

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Demo

Example workflow:

• Ask the assistant to review a diff using repository history.
• The server retrieves similar past review context.
• The model returns grounded feedback aligned to team expectations.

Usage Analytics

To help us understand adoption, the MCP server collects privacy-first, anonymous telemetry on deployments. Future hosted deployments will expose HTTP endpoints (/stats and /ping) that publicly display the number of unique users.

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🧰 Core Tools Reference

The server exposes 12 core tools for IDE agents and developers. For a deep dive on when to use each, see the Tool Strategy Guide.

Tool	Action
ensure_repo_ready	Index a repo and ensure it's ready for queries
generate_repo_rules	Synthesize .cursorrules / CLAUDE.md from PR history
generate_code_from_history	Write code grounded in past commits & team style
review_code_with_history	Perform AI review grounded in team review memory
get_team_review_patterns	Summarize recurring team standards (e.g. "no magic numbers")
semantic_search_reviews	Search past PR comments by meaning, not just keywords
set_active_repo	Switch between multiple indexed repositories
list_indexed_repos	View all repos currently in local/temporary storage
delete_repo_index	Free up disk space by clearing repository indices
get_index_stats	Verify if a repo index is complete (doc count)
update_settings	Update tokens/LLM keys (Hosted mode only)
get_usage_stats	View adoption metrics and unique user counts

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Documentation

Detailed guides are split into focused pages:

• Quick Start and Usage
• LLM Configuration
• Integrations
• Architecture and Tools
• Pipeline Deep Dive
• Configuration Guide (Change Tokens/Settings)
• Roadmap

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Quick Links

• Access setup: GitHub Token Guide
• Client connection: Integrations

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📣 Community & Feedback

We want to hear from you—whether you are a solo developer or a team at a large company!

👤 For Individuals

• Feedback: Please open an issue or start a discussion if you have ideas or encounter bugs.
• Show your support: If this tool saves you time, give it a Star ⭐! It helps others find the project.

🏢 For Corporate & Teams

• Usage: Is your team using this MCP server? Join our "Adopters" list by opening a PR to add your team's name.
• Corporate Feedback: Open an issue with the corporate-usage label to tell us how this has improved your PR review workflow.
• Custom Integration: Need help deploying this to your private cloud? Reach out via GitHub Discussions.

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📜 Documentation & Guides

• Strategy & Best Practices: Tool Strategy & Selection Guide
• Architecture: Architecture and Tools
• Pipeline: Pipeline Deep Dive
• Usage: Quick Start and Usage

🛠️ Troubleshooting

• "command not found": Use absolute paths in your configuration. Run github-pr-context-mcp config to get your exact path.
• "PermissionError: [WinError 32]": The binary is locked by a running process. Close Claude/Cursor, run taskkill /F /IM github-pr-context-mcp.exe, then retry the upgrade.
• Rate Limit Errors: Ensure your GITHUB_TOKEN is valid and has repo scope.

⚖️ License

MIT