Overview

The My Git Project MCP server is a lightweight bridge that exposes the contents and history of a local Git repository to AI assistants. By presenting a structured API over the repository, it allows an assistant such as Claude to query files, retrieve commit logs, and understand the project’s architecture without direct access to the file system. This solves a common pain point for developers who want to leverage AI for code review, documentation generation, or automated issue triage while keeping the repository private and secure.

The server implements a set of intuitive resources that mirror typical Git concepts: files, commits, branches, and diffs. Each resource is represented by a simple, version‑agnostic schema that the AI can request via standard MCP calls. For example, a client can ask for the current state of or request the diff between two commits. The server handles all Git plumbing internally, translating those requests into native Git commands and returning the results in a machine‑readable format. This abstraction removes the need for developers to write custom scripts or maintain complex tooling; the AI can interact with the repository as if it were a native data source.

Key capabilities include:

• File retrieval – fetch the exact contents of any file at a given commit or branch.
• Commit history – list commits, show author information, and access commit messages for context or automated changelog generation.
• Branch management – discover all branches, determine the current branch, and compare branch differences.
• Diff generation – obtain line‑by‑line changes between commits, useful for automated pull request reviews or diff‑based summarization.

These features enable a range of practical use cases. A developer can ask the AI to “explain the changes introduced in the last 5 commits” and receive a concise summary, or request “generate unit tests for the new function.” For continuous integration pipelines, the AI can automatically pull in code coverage reports and suggest improvements. In a pair‑programming scenario, the assistant can surface relevant documentation snippets from the repository, helping new contributors onboard quickly.

Integration into existing AI workflows is straightforward. The MCP server runs as a local or remote service, exposing its capabilities through the standard Model Context Protocol. An assistant can invoke these resources via simple prompt directives, and the server will return structured data that the assistant can embed directly into its responses. Because the protocol is stateless and language‑agnostic, any AI platform that supports MCP can consume this server without modification.

What sets this MCP server apart is its focus on Git, a ubiquitous version‑control system. By providing a dedicated, high‑level interface to repository metadata and content, it eliminates the need for generic file‑system adapters or custom Git wrappers. Developers gain a single, well‑documented endpoint that delivers precise, version‑controlled information to their AI tools, enabling smarter code analysis, automated documentation, and more efficient collaboration.