> Simply change the domain from github.com or github.io to gitmcp.io and get instant AI context for any GitHub repository.

What does this mean? How does it work? How can I understand how it works? The requirements, limitations, constraints? The landing page tells me nothing! Worse, it doesn't have any links or suggestions as to how I could possibly learn how it works.

> Congratulations! The chosen GitHub project is now fully accessible to your AI.

What does this mean??

> GitMCP serves as a bridge between your GitHub repository's documentation and AI assistants by implementing the Model Context Protocol (MCP). When an AI assistant requires information from your repository, it sends a request to GitMCP. GitMCP retrieves the relevant content and provides semantic search capabilities, ensuring efficient and accurate information delivery.

MCP is a protocol that defines a number of concrete resource types (tools, prompts, etc.) -- each of which have very specific behaviors, semantics, etc. -- and none of which are identified by this project's documentation as what it actually implements!

Specifically what aspects of the MCP are you proxying here? Specifically how do you parse a repo's data and transform it into whatever MCP resources you're supporting? I looked for this information and found it nowhere?

Some context.

1. Some LLMs support function calling. That means they are given a list of tools with descriptions of those tools.

2. Rather than answering your question in one go, the LLM can say it wants to call a function.

3. Your client (developer tool etc) will call that function and pass the results to the LLM.

4. The LLM will continue and either complete the conversation or call more tools (functions)

5. MCP is gaining traction as a standard way of adding tools/functions to LLMs.

GitMCP

I haven't looked too deeply but I can guess.

1. Will have a bunch of API endpoints that the LLM can call to look at your code. probably stuff like, get_file, get_folder etc.

2. When you ask the LLM for example "Tell me how to add observability to the code", the LLM can make calls to get the code and start to look at it.

3. The LLM can keep on making calls to GitMCP until it has enough context to answer the question.

Hope this helps.

We built an open source remote MCP server that can automatically serve documentation from every Github project. Simply replace github.com with gitmcp.io in the repo URL - and you get a remote MCP server that serves and searches the documentation from this repo (llms.txt, llms-full.txt, readme.md, etc). Works with github.io as well. Repo here: https://github.com/idosal/git-mcp

For those unfamiliar, this is similar to taking a codebase and processing it with a tool like gitingest, which transforms the entire repository into a format suitable for LLMs, eabling contextual convos and queries about the code. The additional component here is the integration of the MCP protocol, allowing any compliant model to interact with the provided MCP server and dynamically query the codebase to answer questions in real time.

Not sold on MCP being the right paradigm (we'll see), but had a lot of fun building an MCP server recently using https://github.com/tadata-org/fastapi_mcp to quickly get up and running and be able to call to it from Cursor.

I see the appeal of it. It is a good start. But I don't think it's quite useful yet. This proves to be a great distribution model for an MCP project.

FWIW, this project creates two tools for a GitHub repo on demand

  fetch_cosmos_sdk_documentation
  search_cosmos_sdk_documentation

These tools would be available for the MCP client to call when it needs information. The search tool didn't quite work for me, but the fetch did. It pulled the readme and made it available to the MCP client. Like I said before, it's not so helpful at the moment. But I am interested in the possibilities.