"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.

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.

Autonomous AppSec & DAST code and website security scanner across 12 security domains (260+ controls) with 3-Layer Triage.

Enables recurring website maintenance checks — domain registration, SSL certificates, uptime, technical SEO and accessibility — across a portfolio of client sites, using publicly available information. Results are returned as structured data and can be sorted by urgency into a client-ready report.

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.

Lets an AI assistant statically scan HTML and JavaScript landing-page code that turns Meta's fbclid URL parameter into the _fbc cookie (and hand-written _fbp cookies), flagging known ways the click ID is changed, truncated, or kept from an older ad, without executing the code or contacting Meta.

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 coding agents to gate code modifications behind an evidence check, preventing unnecessary edits by requiring reproduction of the problem before allowing changes.

Enables coding agents to compile supplied files, run optional focused tests, and get static findings across ten programming languages, providing a deterministic feedback loop on the code they are about to return.

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.

Local-first data quality validation for Excel/CSV: schema-aware checks with 10 zero-code constraint types (uniqueness, ranges, foreign keys and more) and row-level error reports.

Exposes six evidence-oriented tools over stdio so MCP clients can navigate unfamiliar code, ask typed questions about repository evidence, review completed changes, and select existing tests.

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.

Enables developers to diagnose why FSE 2.0 CDA2 documents are rejected by validating locally, explaining schematron/XSD errors, looking up code systems, and generating synthetic sample documents.

Enables AI coding agents to build and audit Garry's Mod GLua addons with source-aware search, static linting, net security vulnerability scanning, performance profiling, and headless testing.

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.

Diffgate MCP server acts as a code review engine, enabling AI coding agents to analyze and validate code diffs before application. It enhances AI workflows by providing self-checking capabilities to optimise and secure code changes.

Zero-Config, Fully AI-Managed End-to-End Testing for all code gen platforms.

A ruthless, read-only code review MCP server that analyzes code produced by AI coding assistants, catching missing requirements, security vulnerabilities, edge cases, and performance issues without ever modifying files.