"Using Azure DevOps for Task Queries and Code Reviews" matching MCP servers:

Enables AI coding assistants to run static analysis on GitHub repositories, raw code snippets, or local directories to detect cloud cost leaks in Terraform/AWS configs, PostgreSQL migration table-lock hazards, and OWASP/Next.js API security gaps. Each finding returns structured severity, line-level detail, and drop-in fixes.

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.

Enables semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.

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

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

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

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

Enables efficient analysis of large codebases using Claude Code and Kimi K2.5 through the Model Context Protocol, with session caching and parallel processing to reduce costs and time.

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.

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.

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.

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.

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.

Checks a code repository without running it: Safe, Works and Complete scores with an A-F grade, catching leaked keys, dangerous calls, invented or lookalike packages, broken imports and missing project files. Standard-library Python; MCP tools scan, orphans and explain.

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.

Analyzes codebases from local directories, GitHub, and Azure DevOps, providing intelligent context to AI coding assistants through repository structure, critical files, and semantic maps.

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.

Analyzes codebases using Repomix and LLMs to provide structured code reviews with specific issues and recommendations, supporting multiple LLM providers including OpenAI, Anthropic, and Gemini.

Enables AI assistants to perform comprehensive code reviews of local uncommitted changes by combining git diffs with static analysis from linters like ESLint and TypeScript. Returns structured JSON feedback with findings, suggestions, and quality assessments powered by Google's Gemini CLI.

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.

Strict AI code reviewer powered by Groq. Finds bugs, vulnerabilities and security issues in your code. Supports analyze, compare versions, explain code, generate tests, and HTML reports.

Enables AI assistants to search, analyze, and understand multi-language codebases by providing indexed code intelligence via MCP.

Provides comprehensive code quality analysis with quantitative metrics, historical trends, and refactoring risk prediction for C#, Python, and TypeScript codebases.

Intelligent code indexing and analysis for Large Language Models, enabling advanced search, file analysis, and real-time monitoring of codebases.

A self-hosted semantic code retrieval MCP server that enables natural language queries to find relevant code snippets using Tree-sitter AST parsing, embedding, and hybrid search with reranking.