Lao Ma Xiao Zhang's test: GitMCP is amazing! One line of URL makes AI understand your GitHub project in seconds

Like everyone else, I am also a "coder" who deals with code every day. However, in addition to burying my head in writing bugs... no, writing code, I also have a little hobby: I like to figure out the tricks behind technology, see how they work, and what "pain points" in our daily work can be solved . After all, technology, this thing, must eventually be put into practice, and it is valuable only when it helps us work more efficiently, right?

AI programming assistants are becoming more and more popular recently. Tools like Claude and Cursor can really help a lot sometimes. But to be honest, when you use them, do you have the same feeling as me: Why does this AI seem like a "silly naive person"?

You ask it: "Help me check if there is any problem with this code?" It may give you some general suggestions. You ask it again: "Based on the XX module of our project, give me an idea of ​​how to implement a new function?" It starts to say "Uh..." "Ah..." "According to general practice..." and then evades the question.

Why? Because it doesn't understand your project at all ! It doesn't know your code structure, core logic, dependencies, or even the important conventions written in your project README. It's like asking a stranger you just met to decorate your house. What reliable suggestions can he give? At most, it's just some empty words like "modern and simple style is good" and "good lighting".

What can we do every time we encounter this situation? Crazy copy and paste! Throw the project structure, key code, README document, and give it to AI all at once, hoping that it can "understand" a little bit. But this is too inefficient, and the context window always explodes. Isn't it annoying?

Just when I was scratching my head, wondering if there was any good way to make AI "grow its mind" and truly understand our project, hey, I discovered an interesting little thing - GitMCP ^[1] .

What is GitMCP? In one sentence: Give your GitHub project an "AI-powered translator"

Look at the slogan on its official website (although it is very concise): "Instantly create a Remote MCP server for any GitHub project". Translated, it means: For any GitHub project, immediately create a remote MCP server.

Does it sound a bit technical? Don’t worry, it’s actually very simple when you break it down.

Its core function is to enable AI assistants that support the MCP protocol to directly "read" and "understand" the information in the GitHub repository you specify through a special URL.  In this way, when you talk to AI about your project, it will be able to follow your conversation, and the suggestions and codes it gives will be more reliable.

Isn’t it interesting? It feels like giving AI a “project navigator”, or in other words, giving it a detailed “project specification”.

How to use it? It's ridiculously simple!

What impressed me most about this thing is its ease of use. There is no need for complicated configuration, it is divided into two steps:

Step 1: Change your GitHub repository URL

• • If your warehouse address is: https://github.com/username/repo Change it to: https://gitmcp.io/username/repo
• • If your project uses GitHub Pages, the address is: https://username.github.io/repo Change it to: https://username.gitmcp.io/repo

Did you find a pattern ? github.com Replace gitmcp.io, or github.io Replace the previous part of the domain name with gitmcp.io. It's that simple!

Step 2: Feed this new URL to your AI assistant

Many AI tools now support so-called "custom knowledge bases" or "external data sources". GitMCP uses a protocol called  MCP (Model Context Protocol)  . You need to find options such as "Add MCP Server" or "Custom MCP Endpoint" in your AI tool settings, and then add the modified gitmcp.io Fill in the URL.

For example, in Claude, you might find a place to add external knowledge sources in the settings; in the Cursor Editor, there are similar configuration items. The specific operations may vary from tool to tool, but the core idea is to tell the AI: "Hey, for information about this project, go to this gitmcp.io Address asked. "

Done!

Now, when you chat with the configured AI about issues related to the project, the AI ​​will automatically access the GitMCP URL you provided, obtain project background information, and then answer your questions based on your questions.

The whole process is like this:

The magic behind it: What do MCP and GitMCP read?

You may be curious about how GitMCP makes AI “understand” the project. It relies on  MCP (Model Context Protocol) .

You can think of MCP as a "language specification that AI can understand". It defines a standard format that allows external tools (such as GitMCP) to package project information (such as code structure, documentation, key configuration, etc.) into a format that AI can efficiently parse and use.

The GitMCP tool plays the role of a "middle translator". It goes to your repository based on the GitHub URL you provide, and looks for files that it thinks are most helpful for AI to understand the project . According to the official statement, it focuses on the following:

1. 1. README.mdREADME: This is usually the "facade" of the project, including core information such as project introduction, how to install, basic usage, etc. Once AI understands README, it basically has a general impression of the project.
2. 2. llms.txt and llms-full.txt: These two files are of special concern to GitMCP. I guess,llms.txt It may be that you are asked to put some concise "tips" or "context summaries" specifically for AI , such as the core architecture of the project, key conventions, and a list of important APIs. llms-full.txt It may be a more detailed version. If you create these two files in your project, GitMCP will read them first, which is equivalent to giving you a chance to customize the AI's understanding level. Think about it, you can write the most important rules in the project and the places where you least want the AI ​​to make mistakes, so as to guide the AI ​​to take fewer detours.
3. 3.  Other possible files : Although the official website does not specify, I guess it may also intelligently analyze some project structure information, or other common configuration files (such as package.json, pom.xml etc.) to get richer context.

In short, GitMCP uses the MCP protocol to feed the key information "scraped" from your GitHub repository to AI in a structured manner. After AI "eats" this information, it will naturally be much better than the previous "naive" state.

What's so good about this thing? Why is it worth trying?

After chatting for a long time, let’s summarize several core advantages of GitMCP:

1. 1.  Context-aware, AI understands you better : This is the greatest value. Let AI answer questions and generate code based on real project information, which greatly improves accuracy and practicality. Say goodbye to generalities and get to the heart of the project.
2. 2.  Instant setup, zero configuration cost : Just change the URL and fill it into the AI ​​tool, it's that simple. No need to install software, no need to run local services, extremely friendly to novices.
3. 3.  Universal access and wide support : As long as it is a public GitHub repository (including GitHub Pages), it can be used in theory. And it is compatible with mainstream MCP AI tools (such as Claude, Cursor, Windsurf, and even some VSCode plug-ins), with many choices.
4. 4.  Improve efficiency and say goodbye to copy and paste : It saves the trouble of manually feeding AI with a lot of background information, allowing you to focus more on asking questions and getting effective answers.

Comparison of similar solutions

Of course, GitMCP is not the only way to make AI understand project context. There are other solutions on the market. Let’s compare them briefly:

In simple terms:

• •  Want to quickly and easily let AI (Claude/Cursor, etc.) have a basic understanding of a GitHub public project?  GitMCP is very convenient.
• •  Want AI for your local private projects, or need very deep, real-time code understanding?  Maybe IDE plugins (such as Cursor itself, or Copilot Chat in VSCode) are more suitable because they can scan your local files directly.

GitMCP is more like a lightweight "project information accessor", which is particularly suitable for AI tools that support the MCP protocol but do not have powerful local code indexing capabilities.

Some thoughts and considerations

Although GitMCP looks beautiful, Lao Ma Xiao Zhang also reminds everyone to pay attention to a few points:

1. 1.  Currently only public repositories are supported : If your code is private, GitMCP cannot help you for now.
2. 2.  The depth of the context depends on the GitMCP implementation : which files does it read? To what extent is it analyzed?llms.txt What are the best practices? These details still need to be further clarified by official documents or community practices. The ultimate level of AI understanding depends largely on the quality of work of GitMCP, the "translator".
3. 3.  Third-party service dependency : Your AI needs to go through GitMCP's server to obtain project information. This means you need to trust this service, and its stability and speed will affect your experience. © 2025 GitMCP, it looks like a new project, and its future development and maintenance strategies are also worth paying attention to.
4. 4.  Is it free or paid?  Currently it is free to use, but it is not clear whether this will change in the future.

Finally, let me nag

Technology is always evolving. How AI programming assistants can better integrate into our development process and understand the project context is definitely a key link. GitMCP provides a very novel and convenient idea. Through a simple URL conversion, it builds a bridge between AI and GitHub projects.

Although it may not be a silver bullet to solve all problems, it is useful for those who want to quickly let AI tools such as Claude and Cursor "recognize" a public project, or want to llms.txt In the scenario of accurately "feeding" key project information to AI, GitMCP is undoubtedly an interesting tool worth trying.

How about this, are you interested in this GitMCP? Or how do you usually make AI understand your projects?  Welcome to leave a message in the comment area and communicate with Lao Ma Xiao Zhang!

Well, that's all for today. I'm Lao Ma Xiao Zhang, a technical person who likes to study technology and hopes to use technology to make life and work easier. I'll talk to you next time if I have something new and interesting! See you later!