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An AI-powered multi-agent MCP server that auto-reviews GitHub repos. It analyzes commits, detects vulnerabilities, suggests fixes, generates documentation, and creates PRs with human approval. Built with local LLMs for a fully automated, privacy-first code review workflow.
CodeReview-MCP is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 28/100. An AI-powered multi-agent MCP server that auto-reviews GitHub repos. It analyzes commits, detects vulnerabilities, suggests fixes, generates documentation, and. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding CodeReview-MCP to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/kumarvarun3162/CodeReview-MCP.gitReview the upstream source before running this command.
Executes inline Python code, which can hide malicious behavior in a compact payload.
python -c "Dispute this flag?Scan coverage: Install metadata and repository README.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "kumarvarun3162/CodeReview-MCP"
}An AI-powered multi-agent MCP server that auto-reviews GitHub repos. It analyzes commits, detects vulnerabilities, suggests fixes, generates documentation, and creates PRs with human approval. Built with local LLMs for a fully automated, privacy-first code review workflow.