MCP Git Explorer

Overview

The MCP Git Explorer server solves a common bottleneck for AI assistants that need to reason about code: the lack of an efficient, token‑aware way to ingest entire repositories. By exposing a lightweight Model Context Protocol interface, it lets Claude (or any MCP‑compatible client) clone a Git repository on demand, walk its file tree, and return a single structured text representation that includes both the raw contents of each file and an accurate token count. This eliminates the need for manual downloads, reduces data transfer overhead, and guarantees that downstream models stay within token limits.

What the server does

When a client calls , the server clones the target repository—supporting both public URLs and private GitLab projects when a personal access token is supplied. It then traverses the tree, ignoring paths matched by or a custom , and skips binary files as well as empty text files. The output is a hierarchically‑structured string that lists directories and files, each accompanied by its content. A parallel tool performs the same traversal but stops before pulling file contents, instead producing a concise report of file counts, directory structure, and an estimated token total computed with OpenAI’s tiktoken library. This estimation is invaluable for gauging whether a full clone will fit within the model’s context window.

Key features in plain language

• Token‑aware enumeration – Counts tokens before downloading, preventing runaway context usage.
• Selective inclusion – Honors /.repomixignore rules and omits binaries to keep the payload lean.
• Private repository support – Authenticates against GitLab using a personal access token, enabling secure access to protected codebases.
• Unified API – Two simple tools ( and ) cover the entire workflow from sizing to full retrieval.
• MCP compatibility – Integrates seamlessly with Claude’s Model Context Protocol, allowing the assistant to invoke these tools as if they were native capabilities.

Use cases and real‑world scenarios

• Code review assistance – A developer can ask Claude to “summarize this repository” and the assistant will fetch the entire codebase in a single call, then generate a concise overview.
• Bug triage – When investigating a defect that may span multiple files, the assistant can first estimate the size to confirm feasibility, then retrieve only the relevant modules.
• Onboarding new contributors – New team members can request a quick map of the repository structure; Claude will provide a token‑counted outline that helps them understand scope before diving in.
• Security audits – Auditors can ask for a token‑estimated snapshot of all source files to assess compliance without downloading every artifact.

Integration with AI workflows

The MCP Git Explorer fits naturally into any model‑driven pipeline. A typical interaction might look like:

1. Estimate – The client calls to confirm that the repository is within token limits.
2. Retrieve – If acceptable, fetches the full contents.
3. Process – The assistant consumes the structured text, performs analysis (e.g., static code analysis, dependency mapping), and returns actionable insights.

Because the server returns a single structured string, downstream models can parse it without additional API calls, preserving context and reducing latency.

Unique advantages

Unlike generic file‑fetching tools, MCP Git Explorer is built around token counting and ignore rules that mirror a developer’s typical workflow. Its tight integration with the tiktoken library means developers can trust that the assistant will never exceed context limits. The ability to handle private GitLab repositories out of the box removes a common friction point, making it suitable for enterprise environments where code is often gated. Finally, the dual‑tool approach—estimate vs. retrieve—provides a pragmatic balance between speed and completeness, empowering AI assistants to make informed decisions before committing resources.