Git Prompts MCP Server

The Ceshine Git Prompts MCP Server is a lightweight, protocol‑compliant service that turns the contents of a Git repository into ready‑to‑use prompts for AI assistants. By interrogating the current state of a project and comparing it to any ancestor branch or commit, the server can automatically generate concise, context‑rich pull‑request descriptions. This solves a common pain point for developers: crafting clear PR narratives that accurately reflect the underlying changes without manual copy‑and‑paste or deep code review.

At its core, the server exposes a single, well‑defined command—. When invoked with an ancestor reference (branch name or commit hash), the MCP client asks the server to compute the diff between that point and the current HEAD. The server then formats the result in JSON or plain text, depending on configuration, and feeds it back to the assistant. The assistant can embed this output directly into PR templates or conversational prompts, ensuring that every pull request contains a succinct summary of the modifications, relevant files, and overall intent.

Key capabilities include:

• Diff‑driven prompt generation: Automatically harvests file changes, additions, and deletions to create meaningful descriptions.
• Flexible exclusion rules: Users can filter out noise (e.g., lock files, ignore patterns) so the prompt focuses on substantive code changes.
• Output formatting options: Supports both machine‑readable JSON and human‑friendly text, allowing seamless integration into various tooling pipelines.
• Zero‑configuration integration: Works out of the box with editors like Zed, where a simple JSON snippet registers the server as a context provider.

Typical use cases span from CI/CD pipelines that auto‑populate PR bodies, to pair programming scenarios where an AI assistant surfaces a quick summary of recent commits. Developers can embed the server into their local editor, command line workflows, or cloud CI services, enabling consistent, reproducible PR descriptions without manual effort.

What sets this MCP server apart is its focus on Git semantics coupled with the Model Context Protocol’s extensibility. By leveraging MCP, the server can be swapped or extended without touching the assistant codebase; developers can add new commands or tweak formatting by simply updating the server configuration. This modularity ensures that teams can adapt the prompt generation logic to evolving project conventions while keeping the AI workflow clean and declarative.