code-review-mcp

MCP server for AI-powered code review using LiteLLM and an OpenAI-compatible LLM proxy. Use it from Cursor (or any MCP client) to review files in your workspace and get inline suggestions.

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Purpose

This repo provides a Model Context Protocol (MCP) server that:

• Reviews code files via an LLM (through your proxy) and writes inline comments with suggestions, bug hints, security/performance notes, and style improvements.
• Lists workspace files so clients can discover what to review.

The server runs in a container (or locally with UV), mounts your project as a workspace, and exposes two tools: review_code_file and list_workspace_files. It is designed to work with Cursor’s MCP integration and any LLM backend exposed as an OpenAI-compatible API (e.g. LiteLLM proxy, OpenAI, Azure, etc.).

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Using it as MCP (e.g. in Cursor)

1. Configure environment

Copy the example env and set your LLM proxy and project path:

cp env.example .env
# Edit .env: LLM_BASE_URL, LLM_API_KEY, LLM_MODEL, PROJECT_DIR

Required variables:

Variable	Description
LLM_BASE_URL	OpenAI-compatible proxy base URL (e.g. https://your-proxy.example.com/v1)
LLM_API_KEY	API key for the proxy
LLM_MODEL	Model name (e.g. gpt-4, claude-3-sonnet, or your proxy’s model id)
PROJECT_DIR	Absolute path to the project you want to review (used for Docker mount)

2. Build the Docker image

./setup.sh
# or: docker compose build

3. Add the server to Cursor’s MCP config

In Cursor: Settings → MCP (or edit your MCP config file, e.g. ~/.cursor/mcp.json or project-level config). Add a server entry like this, replacing the placeholder values with your own:

{
  "mcpServers": {
    "code-review": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e", "LLM_BASE_URL=https://your-llm-proxy.example.com",
        "-e", "LLM_API_KEY=your_llm_proxy_api_key_here",
        "-e", "LLM_MODEL=gpt-4",
        "-v", "/absolute/path/to/your/project:/workspace:rw",
        "code-review-mcp:latest"
      ],
      "description": "AI-powered code review via LLM proxy",
      "disabled": false
    }
  }
}

Important:

• -v /path/to/your/project:/workspace:rw must point to the repo you want to review; the server reads and writes files under /workspace.
• LLM_* env vars in args override any defaults; use the same values as in your .env (or omit and rely on image defaults if you baked them into the image).

After saving, Cursor will list the code-review MCP server and its tools. You can then ask the AI to “review src/main.py” or “list files in src”; it will call review_code_file and list_workspace_files as needed.

4. (Optional) Run without Docker (local)

Install UV and run the server directly:

uv sync
export LLM_BASE_URL=... LLM_API_KEY=... LLM_MODEL=...
export WORKSPACE_DIR=/path/to/your/project   # or PROJECT_DIR for consistency with .env
uv run python src/server.py

For Cursor, you’d point MCP at this process (e.g. via a wrapper script that sets env and runs uv run python src/server.py) instead of the docker run command above.

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MCP tools

Tool	Description
review_code_file	Review a file in the workspace and add inline comments with suggestions and improvements. Options: file_path (required), review_depth (quick / standard / thorough), focus_areas (e.g. "security, performance").
list_workspace_files	List files under the workspace (or a subdirectory). Option: directory (default "."). Ignores .git, node_modules, __pycache__, .venv, venv.

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Setup reference

• Environment: See env.example for all variables. Never commit real secrets; use .env (gitignored) or env vars.
• Docker: ./setup.sh checks Docker/Compose, creates .env if needed, and builds code-review-mcp:latest. Then docker compose up runs the server (stdio MCP).
• Local dev: uv sync && uv run python src/server.py (ensure LLM_* and WORKSPACE_DIR are set).

For more on Cursor MCP, see Cursor MCP documentation.