I Thought MCP Was Just Tool Calling. Then I Built a Repository Intelligence System.
A few weeks ago, I decided to learn MCP (Model Context Protocol).
My assumption was simple:
Build a tool.
Connect it to Claude.
Done.
How hard could it be?
Turns out, I was so wrong.
What started as an attempt to understand MCP eventually turned into RepoMind — a repository intelligence system that gives Claude the ability to understand entire codebases, analyze dependencies, estimate change impact, and help contributors navigate unfamiliar projects.
But before building RepoMind, I had to unlearn what I thought MCP actually was.
The Experiment That Changed My Understanding of MCP
I started with a calculator tool.
Nothing fancy.
Just a tool that Claude could call.
Then I had a weird idea.
Instead of making the calculator behave correctly, I intentionally made it wrong.
Imagine:
a + b = subtraction
Now the question becomes:
If a user asks "What is 5 + 3?", will Claude answer 8 using its own reasoning, or will it use the tool and answer 2?
The answer surprised me.
Claude often preferred its own reasoning.
That tiny experiment taught me something important:
MCP isn't just about building tools.
It's about designing systems where the model knows when and why to use those tools.
The challenge wasn't tool creation.
The challenge was architecture.
The Real Problem
Whether you're onboarding to a new project, contributing to open source, or trying to improve your own codebase, you often spend more time playing detective than developer.
Hours go into searching files, tracing function calls, understanding dependencies, and figuring out where to even start. AI tools help, but they usually need you to manually spoon-feed context from across the repository.
The result? Less time building software and more time asking, "Wait... where is this function even used?" ðŸ˜
The challenge isn't building software anymore, It is navigating the thousands of lines that have already been built.
Questions like:
- Where does execution start?
- Which files are important?
- Where is this function used?
- What breaks if I modify this module?
- How does data flow through the system?
sound simple.
But answering them usually means:
- Opening dozens of files
- Following imports manually
- Tracing function calls
- Reading documentation
- Searching through folders
Modern AI tools help, but they still require manually feeding repository context into the model.
The bigger the repository becomes, the worse this experience gets.
The problem isn't generating code anymore.
The problem is understanding code.
Why Existing AI Tools Struggle
Most AI assistants can only reason over the information currently available in their context window.
Repository-level questions are different.
They require:
- Repository exploration
- Cross-file reasoning
- Dependency analysis
- Architecture understanding
Questions like:
What files will be affected if I modify this module?
or
Where should a new contributor start?
cannot be answered reliably using a few pasted code snippets.
The model needs access to the repository itself.
That realization became the foundation of RepoMind.
Introducing RepoMind
The idea was simple:
What if Claude could explore an entire repository instead of relying on manually pasted code?
What if repository analysis became a tool instead of a manual task?
RepoMind combines MCP, retrieval systems, dependency intelligence, and specialized agents to give Claude repository-level understanding.
Instead of providing context manually, users simply provide a GitHub repository URL.
Then they can ask questions like:
Where is this function used?
What breaks if I modify this file?
Explain the architecture of this project.
Where should a new contributor start?
Want to see RepoMind in action? I've attached a short demo showcasing the work. For a deeper dive into the implementation, feel free to explore the GitHub repository.
Demo: https://github.com/kanika10-hub/REPO-INTELLIGENCE-RepoMind-/tree/main/DEMO
GitHub: https://github.com/kanika10-hub/REPO-INTELLIGENCE-RepoMind-
How RepoMind Works
The workflow looks like this:
GitHub URL
↓
Clone Repository
↓
Repository Analysis
↓
Dependency Intelligence
↓
Vector Database Indexing
↓
Specialized Agents
↓
Claude Answers Questions
Once the repository is processed, Claude no longer needs additional context.
RepoMind provides it automatically.
Building the Retrieval Layer
The first major challenge was retrieval.
A repository cannot simply be thrown into an LLM.
So I built a repository ingestion pipeline.
The flow became:
Repository
↓
File Collection
↓
Chunking
↓
Embedding Generation
↓
ChromaDB
The project uses:
- Repository ingestion
- Semantic chunking
- Embeddings
- ChromaDB vector storage
- Repository memory
This allowed Claude to retrieve relevant information from across the repository.
But I quickly discovered another problem.
Why RAG Wasn't Enough
Traditional RAG works well for documents.
Repositories aren't documents.
Repositories are networks.
Files depend on other files.
Functions call other functions.
Modules interact with other modules.
A vector database can retrieve relevant code.
It cannot explain how the repository is connected.
That required another layer.
Adding Repository Intelligence
To understand repository relationships, I started building specialized tools.
Eventually RepoMind grew into a system with more than 30 repository-aware tools.
Some examples include:
- Repository analysis
- Dependency graph generation
- Function extraction
- Class extraction
- Impact analysis
- Dependency chains
- Critical file detection
- Semantic search
Instead of viewing repositories as text, RepoMind treats them as systems.
This enabled questions such as:
What breaks if I modify this file?
which traditional retrieval systems struggle to answer.
The Agent Layer
After building the tools, another issue appeared.
Not every repository question should be solved the same way.
An onboarding question requires different reasoning than an impact-analysis question.
So I introduced specialized agents.
RepoMind eventually evolved into a system with seven agents:
Onboarding Agent
Helps contributors understand:
- Entry points
- Key files
- Learning path
Explanation Agent
Explains architecture and code flow.
Impact Analysis Agent
Predicts consequences of code changes.
Bug Investigation Agent
Uses repository context to assist debugging.
Orchestrator
Routes questions to the most suitable agent.
This transformed RepoMind from a collection of tools into an actual repository intelligence platform.
My Favorite Demo
One of my favorite questions is:
What breaks if I modify this file?
Most AI assistants cannot answer this without extensive manual context.
RepoMind can:
- Analyze repository structure
- Build dependency relationships
- Identify affected files
- Estimate risk
- Explain why those components are affected
That's the moment when it stops feeling like a chatbot and starts feeling like an engineering assistant.
Biggest Lessons Learned
Building RepoMind taught me several lessons.
MCP Changes Architecture
I originally thought MCP was just tool calling.
It isn't.
The entire architecture changes once tools become first-class components.
RAG Alone Is Not Repository Intelligence
Retrieval is important.
Understanding relationships is equally important.
Good Tools Matter More Than Fancy Prompts
Most improvements came from building better tools, not writing longer prompts.
AI Systems Are Architecture Problems
The majority of engineering effort went into:
- Retrieval
- Orchestration
- Dependency analysis
- Agent routing
- Repository memory
not prompt engineering.
What's Next?
There are still plenty of improvements I want to explore:
- Pull request analysis
- Architecture visualization
- Multi-repository reasoning
- Contribution recommendation systems
- Advanced impact prediction
RepoMind is still evolving.
Final Thoughts
I started this project because I wanted to learn MCP.
I ended up building a repository intelligence system.
The biggest realization wasn't about AI.
It was about software engineering.
Understanding software is often harder than writing software.
And if AI is going to help developers meaningfully, it needs to understand systems, not just generate code.
That's the idea that led to RepoMind.
Thanks for reading!
happy you made it till the end
— Kanika Rathore
AI Enthusiast
