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

Provides tools for AI-powered graph analysis, including relationship extraction, adjacency matrix creation, and network centrality calculations. It enables users to perform complex structural analysis and generate interactive D3.js visualizations from structured data.

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

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.

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.

MCP server for indexing source code from repositories into a Neo4j graph database and enabling Graph RAG-based search and traversal of functions via natural language queries.

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.

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.

Enables AI assistants to review GitLab merge requests by fetching diffs, analyzing code changes, adding comments, and managing approvals through the GitLab API. Supports comprehensive merge request analysis and version comparison for automated code review workflows.

Enables AI assistants to review GitHub pull requests and GitLab merge requests by fetching PR/MR details and diffs, with optional file-type filtering, and posting inline, general, or batched review comments. Supports self-hosted GitLab and custom project-specific review rules through stdio, SSE, or WebSocket transports.

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 AI assistants to review GitLab merge requests by fetching changes, analyzing diffs, adding comments, and managing approvals through the GitLab API. Supports complete merge request analysis, file-specific reviews, and version comparisons.

Integrates GitHub repository management and local file system access to enable AI agents to perform code reviews and manage pull requests. It provides tools for listing repositories, retrieving PR diffs, and searching or reading files within a specified workspace.

LLM routing proxy that cuts API costs 60-90% by auto-selecting the cheapest capable model across OpenAI, Anthropic, and Google. Provides stats, config, and model comparison tools via MCP.

A lightweight, configurable server that fetches coding guidelines, security rules, and validation patterns from external sources to help development teams maintain code quality standards in WordPress projects.

Enables creating interactive data visualizations from natural language queries using DuckDB for local databases or Databricks for enterprise data warehouses. Supports multiple chart types, CSV imports, SQL queries, and automatic statistical analysis through Claude Desktop.

An MCP server that performs code reviews by comparing local source code against design documents stored in Confluence. It integrates with Atlassian MCP servers to analyze documentation and provide suggestions for quality improvement based on design specifications.