"Information about Gemini code" matching MCP servers:

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.

Scaffolds new MCP servers for the OpenSIN-Code ecosystem with templates for Python, Node, Go; provides tools to add tools, test, validate, register, publish, and audit servers.

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

A code review MCP server built on FastMCP, supporting security audit, style check, complexity analysis, and bug pattern detection for Python code.

Code Reviewer AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

MCP server providing code review, auto-fix, test running, linting, formatting, and refactor suggestions for Claude Code.

Provides comprehensive code analysis through three MCP servers: static analysis for code quality and security, dependency analysis for package management and vulnerabilities, and complexity analysis for maintainability assessment across multiple programming languages.

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

Catch Gemini model upgrade regressions before they reach customers by running evals, comparing outputs, detecting regressions, and exporting audit receipts.

Enables AI-assisted code review with bias mitigation strategies through cross-model evaluation and bias-aware prompting. Detects AI-generated code from commit authors and provides structured reviews with security, performance, and maintainability analysis.

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.

Enables AI coding tools to run CheckMyLaunch scans on GitHub repos or live URLs and retrieve findings and repair prompts directly within the chat.

A Model Context Protocol (MCP) server for interacting with SonarQube code quality platform.

MCP server for Allure TestOps — lets an LLM agent (Claude Code, Cursor, Windsurf, etc.) inspect test quality without leaving the chat.

Connects AI coding assistants to Gaffer test history and coverage data to analyze project health, debug failures, and identify untested code areas. It enables tools to track test stability, cluster failures by root cause, and monitor code coverage trends across projects.

Enables agents to verify completion by cold-running an acceptance command and reporting whether it exits with code 0, returning a pass/fail receipt with output and refusal reasons.

Enables independent, read-only adversarial review of candidate test cases and bug findings, returning a review delta that authoring agents reconcile before writing final artifacts.

Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.

Provides an isolated workspace for testing candidate code, runs tests, and returns deterministic pass/fail verdicts. Enables automated grading of software engineering solutions by ensuring reproducible test runs.