"Code Review Graph Visualization Tools and Resources" matching MCP servers:

Enables AI coding assistants to query a persistent local code graph for targeted context, blast-radius analysis, and incremental updates, so they read only relevant files during reviews and large-repo workflows.

Knowledge graph for token-efficient code reviews. Builds a structural map of your codebase with Tree-sitter, tracks changes incrementally, and gives AI agents precise context via MCP tools. Features fixed multi-word search, qualified call resolution, dual-mode embedding (ONNX local + LiteLLM cloud), and output pagination.

Enables Claude Code to query a TypeScript dependency graph and retrieve the minimal set of files needed for code review, implementation, or debugging.

Enables querying cross-repo code dependencies, HTTP routes, database tables, and queues via an MCP server using Cypher queries.

An MCP server that provides local code quality analysis for AI coding assistants, supporting file analysis, git diff review, and full project scanning with quality scoring.

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.

Deterministic code review MCP server that provides tools for file selection, rule matching, comment positioning, and reflection, ensuring stable review quality without LLM calls.

Enables AI assistants to review GitHub and GitLab pull requests and merge requests, supporting multiple transports and custom review rules.

AI-powered code review tool that detects AI-generated code defects invisible to traditional linters — hallucinated packages, deprecated APIs, cross-file contradictions, hidden security anti-patterns, and over-engineering. Works as a standalone CLI, GitHub Action, or MCP server. Supports TypeScript, Python, Java, Go, and Kotlin. Free for individuals, no API key required.

An MCP server that automates code review by providing syntax checking, code explanation, and improvement suggestions using a multi-agent system.

Enables Codex CLI to perform code reviews on uncommitted changes via an MCP tool, keeping review context separate from the main session.

Enables comprehensive GitHub PR reviews through Cursor's AI by fetching PR diffs, running static analysis tools (ESLint, Prettier, TypeScript, Semgrep), executing tests, and generating detailed code review reports with inline comments.

A high-performance code knowledge graph server implementing MCP, indexing codebases into a structured AST knowledge graph with semantic search, call graph traversal, and HTTP route tracing.

A local MCP server that indexes codebases into Neo4j and provides tools for code analysis, dependency visualization, and impact assessment.

An MCP server for automated code review of GitHub pull requests. It checks security, quality, and license issues, and provides tools to list, inspect, and review PRs.

MCP server for automated code review using AI agents. It analyzes code diffs or file paths for bugs, security issues, and style violations.

Enables querying a TypeScript codebase's graph for call flows, type relationships, and symbol locations without reading file bodies.

Turns a codebase into a queryable graph with semantic search, call graphs, and control/data flow analysis, served to AI coding agents via the Model Context Protocol.

Builds rich code graphs from TypeScript/NestJS codebases using AST analysis and Neo4j, enabling semantic search, natural language querying, and intelligent graph traversal to provide deep contextual understanding of code relationships and dependencies.

Open-source AI code review MCP server for local git diff auditing with deterministic security rules and AI-powered analysis using any OpenAI-compatible model.