"Resources or guides for pull request code reviews" matching MCP servers:

Analyzes Git pull request diffs to detect breaking REST and GraphQL API schema changes, providing deterministic AST-level code review and breaking change alerts for CI/CD workflows.

Enables automated semantic analysis of Git pull request diffs to detect breaking REST/GraphQL API schema changes, integrating with MCP-compatible clients for AI-driven code review.

Enables fetching and analyzing Gemini Code Assist reviews from GitHub pull requests, with smart defaults and pagination.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

Reviews every PR for security, bugs, and performance, then fixes the findings through MCP, keeping you in control of what merges.

MCP server with local vector search for your codebase. Smart indexing, semantic search, Git history — all offline.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

embedded code search engine CLI -AST based that just works - improves speed and efficiency for coding agent

An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.

MCP server that guides LLM security reviews step-by-step, tracking progress and ensuring all assigned source code is read before completing each plan step.

Gemini-powered MCP server for automated code review, analysis, and documentation.

An MCP server for intelligently reading Java source code, supporting extraction from Maven dependencies and local projects with dual decompilers.

MCP server for comprehensive code analysis, navigation, and quality assessment across 25+ programming languages.

Indexes a mono-repo into a knowledge graph and provides MCP tools to query code structure—packages, components, routes, HTTP calls—without file reads or grep round-trips.

Enables agents to run semantic search across one or more local project directories by automatically maintaining a LAN-local Qdrant index with Ollama embeddings. Indexing, staleness checks, and incremental updates happen transparently, so users can query code by meaning without managing collections, chunks, or hashes.

AI-powered continuous code assistant for Cursor, VS Code, Antigravity, and Claude Code via MCP, providing tools for code review, testing, documentation, and quality analysis.

Connects Kimi Code with Claude Code, enabling Claude to delegate bulk codebase reading to Kimi (256K context) for cost savings, while Claude focuses on reasoning and code edits.

Wraps Claude Code's code-review skill so any MCP client can run reviews over a PR, branch, working directory, or last commit, with five parallel reviewer subagents, low-to-max effort levels, and optional fix mode that edits files in place. Also supports follow-up questions on a previous review session and a free environment self-check.

Provides structural, queryable understanding of a Python codebase via MCP tools, enabling direct lookups for callers, dependencies, and class hierarchies without repeated grep/read cycles.

An installable MCP server that reviews diffs/PRs against a target repository's own coding conventions, learned via RAG over its code, docs, and lint config, citing specific file:line sources.