A collaborative code and markdown review server that lets humans and AI agents inspect repository files, git diffs, PDFs, and Markdown in a shared browser UI, add anchored comments, and save the review as JSON and Markdown. It can also expose the same review session over MCP so agents can work against the live review state.
From the registry: Git diff and markdown review with a web UI plus MCP stdio for AI agents.

$ git clone https://github.com/hevangel/batch-review-mcp.git && cd batch-review-mcp && uv synchttps://github.com/hevangel/batch-review-mcpPlease install the `batch-review-mcp` MCP server into my current AI client (that's you).
Required prerequisites (do these first if not already done):
- **Install Python and uv** — Python 3.13 or newer and uv are required to run the server. (https://docs.astral.sh/uv/)
- **Install Node.js** — Node.js 22 or newer is required for the one-time frontend build that occurs automatically on first run if frontend/dist does not exist.
- **Clone the repository and install dependencies** — The README only documents running this MCP server from a local clone using uv sync. Run: `git clone https://github.com/hevangel/batch-review-mcp.git && cd batch-review-mcp && uv sync` (https://github.com/hevangel/batch-review-mcp)
Canonical MCP server config (stdio transport):
- command: `uv`
- args: ["run","batch-review","--mcp","--root","<path-to-your-repo>"]
Note: The README documents both stdio and HTTP MCP support, but the repeatable client config shown is stdio. HTTP transport is available from the running app at the /mcp endpoint after starting the server. You may optionally add --no-browser to suppress opening the web UI, and --skip-build after the frontend has already been built.
Add this MCP server to my current client's config in the correct format for you. If you need secrets or credentials I haven't provided, ASK me — do not invent values or leave raw placeholders. After adding it, tell me how to verify the server is connected.Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
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