Stop Wasting AI Context: How to Build an MCP Server That Intelligently Reads GitHub Repos

The Challenge of Large Codebases & Limited AI Context

• The "Firehose" Problem: When you try to give an AI too much information at once, it's like trying to drink from a firehose. The AI can't distinguish between what's important & what's not, & its performance degrades significantly.
• Irrelevant Information: A huge portion of any large codebase is irrelevant to any specific task. An AI doesn't need to know about the billing module to help you with a CSS bug in the user profile section.
• Loss of Nuance: Code is more than just text. It has structure, dependencies, & a history of changes. Simply dumping files into a context window loses all of this valuable information.
• Cost & Latency: Feeding massive amounts of data to an AI is computationally expensive & slow. It's just not practical for real-time development workflows.

The Architecture of an Intelligent MCP Server

1. The GitHub Connector: This is the part of our server that's responsible for cloning or accessing your GitHub repository. It needs to be able to pull down the code & keep it up to date.
2. The Processing Pipeline: This is where the magic happens. This pipeline takes the raw code from your repository & transforms it into a format that's easy for an AI to understand. It involves:
   • Code Chunking: Breaking down the code into smaller, meaningful pieces.
   • Embedding Generation: Converting these code chunks into numerical representations (embeddings) that capture their semantic meaning.
   • Vector Database Storage: Storing these embeddings in a specialized database that allows for efficient similarity searches.
3. The MCP Server Core: This is the heart of our system. It's a server that listens for requests from an AI model. When a request comes in, it:
   • Understands the Query: It takes the natural language query from the AI (e.g., "how do we handle user authentication?") & converts it into an embedding.
   • Finds Relevant Context: It searches the vector database for the code chunks that are most similar to the query embedding.
   • Builds the Context: It compiles these relevant code chunks into a concise, focused context for the AI.
   • Responds to the AI: It sends the context & the original query to the AI, which can then generate a much more accurate & helpful response.

Step 1: Code Chunking - The Art of Breaking Down Your Code

• Fixed-Length Chunking: This is the most basic approach, where you simply split the code into chunks of a fixed size (e.g., every 1000 characters). While it's easy to implement, it's not ideal for code, as it can break up functions, classes, & other logical blocks, destroying the context.
• Recursive Chunking: This method is a bit smarter. It tries to split the code based on a hierarchy of separators, like double newlines (for paragraphs), single newlines, & so on. This is better than fixed-length chunking, but it still doesn't fully understand the structure of code.
• Semantic Chunking: This is where things get interesting. Semantic chunking uses a language model to group related pieces of code together based on their meaning. This is a very powerful technique, but it can be computationally expensive.
• Content-Aware Chunking: This is the sweet spot for code. This approach respects the boundaries of functions, classes, & other syntactical structures. It ensures that each chunk is a self-contained, meaningful piece of code. There are open-source tools that can help with this, breaking down your code around logical breakpoints & ensuring that each chunk maintains its integrity.

Step 2: Embeddings - Turning Code into Numbers

Step 3: Vector Databases - The Library for Your Code's "DNA"

1. You take each of your code chunks & its corresponding embedding & store them in the vector database.
2. When you have a query (e.g., "where do we handle API request authentication?"), you use the same embedding model to convert your query into an embedding.
3. You then use this query embedding to search the vector database. The database will return a list of the most similar code chunks, ranked by their similarity score.

Putting It All Together: The Intelligent MCP Server in Action

1. You ask your AI assistant: "How does our system update product inventory when a new order is placed?"
2. The AI sends a request to your MCP server: The request contains your natural language query.
3. The MCP server gets to work:
   • It takes your query & uses the embedding model to convert it into a query embedding.
   • It searches the vector database for the code chunks that are most similar to your query embedding.
   • The search results might include the
     1 updateInventory
     function, the
     1 Order
     class, & the
     1 Product
     model, all of which are highly relevant to your query.
   • The MCP server then compiles these code chunks into a concise context. It might also include some additional information, like the file paths & line numbers of the code chunks.
4. The MCP server responds to the AI: It sends the context & your original query to the AI.
5. The AI generates a helpful response: Now, instead of having to guess about your codebase, the AI has a highly relevant, targeted context to work with. It can give you a detailed explanation of how inventory is updated, referencing the specific functions & classes in your code. It might even be able to generate some boilerplate code for your new feature, perfectly consistent with your existing patterns.

The Role of a Great User Interface: Bringing it all Together with Arsturn

• Instant Customer Support for Your Dev Team: Think of your codebase as a product & your developers as the customers. An Arsturn-powered chatbot could provide instant, 24/7 support, answering questions about your code's architecture, conventions, & history.
• Engaging with Your Codebase: Instead of just being a passive repository of code, your GitHub repo becomes an interactive, conversational resource. New developers can onboard faster, & experienced developers can be more productive.
• A Personalized Experience: Because the MCP server is providing context from your specific codebase, the chatbot's responses will be highly personalized & relevant. It's not just a generic coding assistant; it's your coding assistant.

Beyond Semantic Search: The Future is Hybrid

• Abstract Syntax Trees (ASTs): An AST is a tree representation of the structure of your code. By creating an index of your code's AST, you can perform more structured searches, like finding all the places where a specific function is called or where a certain class is instantiated.
• Keyword Filtering: Sometimes, you just need to find a specific keyword. By combining semantic search with traditional keyword-based filtering, you can get the best of both worlds: the broad understanding of semantic search & the precision of keyword matching.
• Graph-Based Retrieval: You can also build a dependency graph of your code to understand how different parts of your codebase are connected. This can be incredibly useful for answering questions about the impact of a change or the flow of data through your system.

Wrapping Up