Repo To Txt MCP

Repo‑to‑Txt MCP – Turning Repositories into AI‑Ready Context

Repo‑to‑Txt MCP is a Machine Code Protocol server that transforms Git repositories—whether hosted on GitHub or stored locally—into structured plain‑text artifacts. By flattening the file hierarchy and concatenating source files into a single, well‑formatted document, it delivers an LLM‑friendly representation of codebases that can be injected directly into model prompts. This solves a common pain point for developers: providing large language models with accurate, up‑to‑date repository context without the overhead of parsing complex directory trees or handling binary assets.

The server offers a suite of practical features that make it especially valuable for AI‑centric workflows. It can analyze both remote and local repos, apply fine‑grained file filtering (include or exclude specific extensions), and enforce token limits to keep payloads within model constraints. The output is a clean, human‑readable text file that preserves the original folder structure—making it easy for developers to navigate or for models to reference specific modules. Because the service is built on a standard MCP interface, it plugs seamlessly into existing toolchains such as Cursor or Smithery, enabling conversational AI agents to fetch repository context on demand.

Typical use cases include code review assistants that need the full project view, automated documentation generators that rely on source text, and debugging tools that ingest a repository snapshot to suggest fixes. In continuous‑integration pipelines, the server can be invoked before model inference to ensure that the LLM has access to the latest commit history. Its cross‑platform support and Docker image further simplify deployment in diverse environments, from local machines to cloud functions.

What sets Repo‑to‑Txt MCP apart is its focus on context fidelity and integration ease. By converting repositories into a single, token‑controlled text file, it eliminates the need for custom parsers or heavy preprocessing steps. The MCP contract exposes a minimal API () that accepts source URLs or paths, optional file filters, and a flag to return the generated text as a downloadable file. This lightweight interface means that any AI client—whether a conversational agent or a batch processing script—can retrieve repository context in seconds, streamlining the development cycle and enhancing model performance.