"Using LLM for Automated Code Review and Validation" matching MCP servers:

An MCP server that provides code review functionality using OpenAI, Google, and Anthropic models, serving as a "second opinion" tool that works with any MCP client.

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.

An MCP server that integrates AI assistants like Claude with GitLab's merge requests, allowing them to review code changes, add comments, and approve/unapprove merge requests directly through the GitLab API.

An MCP server that lets Claude Code consult stronger AI models (o3, Gemini 2.5 Pro, DeepSeek Reasoner) when you need deeper analysis on complex problems.

Share code context with LLMs via MCP or clipboard

An MCP server that provides LLMs access to other LLMs

MCP server for E2B code sandbox, enabling creation, management, and execution of code in secure sandboxes using Python and shell commands.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

Provides a universal bridge to interact with any OpenAI-compatible LLM API (local or cloud), enabling model testing, benchmarking, quality evaluation, and chat operations with performance metrics.

A MCP server for managing and storing code snippets in various programming languages, allowing users to create, list, and delete snippets via a standardized interface.

A Model Context Protocol server that helps large language models process code repositories by providing file tree generation, code merging, and code analysis capabilities.

An implementation of Claude Code as a Model Context Protocol server that enables using Claude's software engineering capabilities (code generation, editing, reviewing, and file operations) through the standardized MCP interface.

Enables coding agents to develop on one machine and verify results on another, using branch-bound runbooks, isolated checkpoints, and structured receipts to guide repair iterations.

This project aims to build a Claude Code MCP server and implement its associated tools (explain\_code, review\_code, fix\_code, edit\_code, test\_code, simulate\_command, your\_own\_query). The server is implemented using Node.js and the MCP SDK. It receives tool requests from clients via Stdio, dynamically

Provides tools for AI-driven development workflows including file system operations, code analysis, code execution, web fetching, and search.

An MCP server to create secure code sandbox environment for executing code within Docker containers.

Enables Codex to offload expensive code reading, editing, and checking to a worker agent via Claude Code, supporting async jobs and long-running tasks.

Enables AI assistants to interact with VS Code for language intelligence, debugging, and code execution.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

Enables Claude Science to delegate independent review or implementation tasks to the official OpenAI Codex CLI via an asynchronous queue, avoiding one-minute time limits and sandbox issues.