Code Review MCP

AI-powered code review using Zhipu GLM. The server gathers git diffs and optional source context, then asks the model for a focused review.

Installation

Install dependencies:

git clone https://github.com/Enferlain/review-mcp.git
cd review-mcp
cp .env.example .env
# Optional: edit .env and add your API key
uv sync

Then add it to your MCP client.

For local development, use uv run from your clone:

{
  "mcpServers": {
    "review-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/review-mcp",
        "run",
        "review-mcp"
      ]
    }
  }
}

For day-to-day use from Git, uvx avoids hardcoding a local install path:

{
  "mcpServers": {
    "review-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/Enferlain/review-mcp",
        "review-mcp"
      ]
    }
  }
}

The MCP caller should pass the repository being reviewed as working_directory on each review_with_context call. If your MCP client cannot pass a per-call workspace, you can still start the server with a fixed fallback:

"args": [
  "--from",
  "git+https://github.com/Enferlain/review-mcp",
  "review-mcp",
  "--workspace-dir",
  "/absolute/path/to/the-repo-you-want-reviewed"
]

Related MCP server: Git Code Review MCP

Configuration

Environment variables (in .env):

• AI_API_KEY (required): Your API key

• ZHIPU_API_KEY (optional): Backward-compatible fallback key name

• ZHIPU_BASE_URL (optional): Override API endpoint

• AI_MODEL / ZHIPU_MODEL (optional): Override the review model (default: glm-5.2)

• AI_API_TIMEOUT_SECONDS (optional): Timeout for each model API request (default: 900)

• MAX_REVIEW_ITERATIONS (optional): Max tool-calling iterations (default: 20, capped at 50)

• MAX_REVIEW_CONTEXT_CHARS (optional): Hard ceiling for accumulated model context (default: 90000)

• MAX_REVIEW_TOOL_RESULT_CHARS (optional): Per-tool result ceiling before truncation (default: 20000)

• REVIEW_TOOL_TIMEOUT_SECONDS (optional): End the MCP tool call before the host-level timeout (default: 1800)

• REVIEW_MCP_INCLUDE_TRACE (optional): Append diagnostic trace details to review responses (true/false)

Usage

The MCP exposes 1 tool: review_with_context

Parameters:

• diff_target: 'staged' (default), 'unstaged', or a git ref like 'HEAD~1'

• context_files: Additional files or OpenSpec change folders to include as context

• focus_files: Specific files to focus the review on

• task_description: Description of what you're trying to accomplish

• working_directory: Git repository root to review (required unless the server was started with --workspace-dir)

• include_trace: Include a compact diagnostic trace in the returned review (optional, defaults to REVIEW_MCP_INCLUDE_TRACE)

While a review runs, the MCP tool emits best-effort status/progress updates for major phases such as loading context, preparing scoped diffs, calling the model, retrying transient model errors, running reviewer tools, and waiting during long model calls. MCP clients that display progress or log notifications can show those updates before the final review returns.

When called, it automatically:

1. Reads any files listed in context_files

2. Expands explicitly provided OpenSpec change folders into their context files

3. Resolves render_diffs() and file:/// links inside those context files

4. Includes an initial scoped diff when focus_files or context-file render_diffs() links identify files

5. Lets GLM inspect a bounded repository tree, search with ripgrep, and read targeted line ranges

6. Lets GLM request repository-wide staged and unstaged diffs even when focus_files is active

7. Deduplicates repeated requests/results and enforces cumulative and per-tool context budgets

8. Returns the final review

OpenSpec change folders are included only when the MCP caller passes the folder path in context_files, for example:

{
  "context_files": [
    "/home/imi/Projects/sd-scripts/openspec/changes/resource-intelligence-system"
  ]
}

If MCP calls feel opaque, set include_trace to true for a single call or set REVIEW_MCP_INCLUDE_TRACE=true in the environment. The returned review will include a compact trace with the workspace, diff target, context-file count, payload sizes, model iterations, and tool calls.

Trace output is meant for debugging review behavior and can be disabled again once the setup is behaving as expected.

Codex / VS Code Notes

This server now starts cleanly under MCP hosts because it avoids doing heavy work at import time. A few setup notes still matter:

1. Prefer passing working_directory per tool call so one MCP config can review any repo.

2. If your MCP client cannot inject the current repo automatically, set --workspace-dir in the config as a fixed fallback.

3. Prefer setting AI_API_KEY as a system/user environment variable instead of storing it in MCP config.

4. The tool-level working_directory argument still overrides the configured workspace when your agent provides it.

Example Windows fallback path:

"args": [
  "--directory",
  "D:/Projects/review-mcp",
  "run",
  "review-mcp",
  "--workspace-dir",
  "D:/Projects/myrepo"
]

Example prompt to your AI assistant:

"Review my staged changes"

Security Note

Model-requested tree, search, and file-read tools are confined to the selected repository root, including protection against .. and symlink escapes. Explicit caller-provided context_files may still reference readable files outside the repository, so use those deliberately in sensitive environments.