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

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.

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

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.

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.

Local MCP server for Cursor that analyzes code, finds bugs, generates tests and documentation via OpenRouter.

Enables AI agents to submit code snippets for review and receive structured quality reports covering correctness, security, performance, maintainability, and best practices.

An MCP server that provides senior-level code review, quality checks, security analysis, and refactoring suggestions directly in your editor.

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.

Runs a multi-agent code review pipeline via MCP, analyzing git diffs for security, quality, and logic issues and returning a pass/fail verdict.

Enables Claude to perform thorough code reviews by integrating with Codex and Gemini CLIs to provide senior-level feedback on code quality, security, performance, and best practices. Supports reviewing code snippets, individual files, or entire directories with automatic detection of available review tools.

Enables local candidate review of ONNX or TFLite models by checking static evidence and externally measured results against a fixed policy, producing accept/reject/hold evidence ZIPs for AI agent workflows.

Enables execution and monitoring of Cucumber.js + Playwright automated tests, including real test runs, status checks, feature discovery, and coverage analysis.

MCP server that provides tools for evaluating LLM agent reliability, including adversarial task generation, automated LLM-as-judge assessment, and confidence statistics.

An MCP server that enables code review by having two LLMs (Claude and GPT-4o) independently evaluate code and then synthesize their findings into a unified report.

A local MCP server that packages LLM evaluation gates as reusable CI/CD primitives, enabling AI agents to run datasets against models, score responses, and enforce quality thresholds.

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.

Enables visual review of frontend UI variants by providing an MCP tool that opens, refreshes, verifies, captures, and closes review surfaces.

Enables AI coding agents to receive continuous, structured software-quality feedback from Jev across multiple quality dimensions while working locally.