How do you make AI understand all your code or your data repository? Meet the Model Context Protocol (MCP) ✨
Machine translation from the original post. Not reviewed by a human translator. Historical claims may no longer be current.
How do you make AI understand all your code or your data repository? Meet the Model Context Protocol (MCP) ✨
When working with models like Claude, one of the main problems is that they don't "see" the whole context of the project. They can analyze a single file, but understanding a whole project is a completely different task.
Until now, the common solutions were to copy and paste code manually or to use vector stores (Vector Search) to look up relevant information.
But what if there were a better way?
This is where the Model Context Protocol (MCP) comes into the picture - an innovative protocol that lets models access additional information dynamically and in a controlled way.
How does MCP work?
Instead of providing the model with static contexts, MCP acts like a "smart broker" between the model and external information:
1️⃣ The model identifies a need for additional information - for example, when it is asked a question about a certain function in the code.
2️⃣ The model sends a request to an MCP server, which is connected to information sources like GitHub, internal documents or an external API.
3️⃣ The server returns to the model only the relevant information, without flooding it with unnecessary details.
💡 The big advantage?
The model gets live access to up-to-date information, and doesn't depend only on what was loaded into the initial prompt.
Here are examples of real uses:
📌 Integration with existing code projects
Instead of pasting code manually, you can use MCP to pull relevant files and understand complex relationships.
For example:
You ask the model: "What does the function process_data() do?"
MCP identifies that it depends on the file utils.py, pulls it, and only then can the model explain the full logic.
📌 Creating smart Pull Requests
When you want to make a change in a third-party library, MCP can help understand how the change will affect the whole code and generate a PR automatically.
📌 Connecting to internal organizational documents
If you need to quickly pull information from API documents or internal documentation, MCP lets the model access the most up-to-date information directly - instead of relying on outdated or partial information.
So it's no wonder MCP is becoming a standard, and you can already find Github libraries that gather MCP tools, and even Claude announced that they officially support this protocol - this is a significant step forward that will make working with AI models much smarter and more efficient.
Have you already used such a model? Know a cool MCP model? Tell me!
