MCP Codebase Index Server

AI-powered semantic search for your codebase in GitHub Copilot, Kiro, and other MCP-compatible editors

A Model Context Protocol (MCP) server that enables AI editors to search and understand your codebase using Google's Gemini embeddings and Qdrant vector storage.

Supported Editors:

• ✅ VS Code with GitHub Copilot
• ✅ VS Code with Roo Cline
• ✅ GitHub Copilot CLI
• ✅ Google Gemini CLI
• ✅ Kiro AI Editor
• ✅ Any MCP-compatible editor

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📚 Quick Navigation

🚀 Getting Started

• 📖 Full Documentation - Complete documentation
• ⚙️ Setup Guide - VS Code - Installation for VS Code Copilot
• 🖥️ Setup Guide - CLI - Installation for GitHub Copilot CLI
• 🤖 Setup Guide - Gemini CLI - Installation for Google Gemini CLI
• 🎯 Setup Guide - Kiro - Installation for Kiro AI Editor
• 🦘 Setup Guide - Roo Cline - Installation for Roo Cline (VS Code)
• ⚡ Quick Reference - Command cheat sheet
• 🗺️ Navigation Guide - Find any doc quickly

💻 For Developers

• Source Code Structure - Code organization
• MCP Server Guide - Build your own MCP server
• Roadmap - Future plans

🔧 Resources

• Qdrant Setup - Get Qdrant credentials
• Testing Guide - Test search functionality
• Prompt Enhancement Guide - Use prompt enhancement effectively
• Vector Visualization Guide - Visualize your codebase
• Changelog - Version history

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✨ Features

• 🔍 Semantic Search - Find code by meaning, not just keywords
• 🎯 Smart Chunking - Automatically splits code into logical functions/classes
• 🔄 Incremental Indexing - Only re-indexes changed files (90%+ time savings)
• 💾 Auto-save Checkpoints - Saves progress every 10 files, resume anytime
• 📊 Real-time Progress - Track indexing with ETA and performance metrics
• ⚡ Parallel Processing - 25x faster indexing with batch execution
• 🔄 Real-time Watch - Auto-updates index on file changes
• 🌐 Multi-language - Supports 15+ programming languages
• ☁️ Vector Storage - Uses Qdrant for persistent storage
• 🤖 Prompt Enhancement - AI-powered query improvement (optional)
• � Vector Visualization - 2D/3D UMAP visualization of your codebase
• 🏗️ Modular Architecture - Clean handler separation for maintainability
• �📦 Simple Setup - Just 4 environment variables

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🚀 Quick Start

Prerequisites

1. Gemini API Key - Get free at Google AI Studio
2. Qdrant Cloud Account - Sign up free at cloud.qdrant.io

Installation

Choose your environment:

• VS Code Users: Follow steps below or see Roo Cline Setup
• Copilot CLI Users: See Copilot CLI Setup Guide
• Gemini CLI Users: See Gemini CLI Setup Guide
• Kiro Users: See Kiro Setup Guide

Step 1: Open MCP Configuration in VS Code

1. Open GitHub Copilot Chat (Ctrl+Alt+I / Cmd+Alt+I)
2. Click Settings icon → MCP Servers → MCP Configuration (JSON)

Step 2: Add this configuration to mcp.json:

{
  "servers": {
    "codebase": {
      "command": "npx",
      "args": ["-y", "@ngotaico/mcp-codebase-index"],
      "env": {
        "REPO_PATH": "/absolute/path/to/your/project",
        "GEMINI_API_KEY": "AIzaSyC...",
        "QDRANT_URL": "https://your-cluster.gcp.cloud.qdrant.io:6333",
        "QDRANT_API_KEY": "eyJhbGci..."
      },
      "type": "stdio"
    }
  }
}

Step 3: Restart VS Code

The server will automatically:

• Connect to Qdrant Cloud
• Index your codebase
• Watch for file changes

📖 Detailed instructions:

• VS Code Setup Guide
• Copilot CLI Setup Guide

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📖 Usage

Search Your Codebase

Ask GitHub Copilot:

"Find the authentication logic"
"Show me how database connections are handled"
"Where is error logging implemented?"

Visualize Your Codebase

Ask GitHub Copilot:

"Visualize my codebase"
"Show me how my code is organized"
"Visualize authentication code"

📖 Complete guide: Vector Visualization Guide

Check Indexing Status

"Check indexing status"
"Show me detailed indexing progress"

📖 More examples: Testing Guide

📊 Vector Visualization

See your codebase in 2D/3D space - Understand semantic relationships and code organization visually.

What is Vector Visualization?

Vector visualization transforms your codebase's 768-dimensional embeddings into interactive 2D or 3D visualizations using UMAP dimensionality reduction. This allows you to:

• 🎨 Explore semantic relationships - Similar code clusters together
• 🔍 Understand architecture - See your codebase structure at a glance
• 🎯 Debug search results - Visualize why certain code was retrieved
• 📈 Track code organization - Identify modules, patterns, and outliers

Quick Start

Visualize entire codebase:

User: "Visualize my codebase"

Result: Interactive clusters showing:
- API Controllers & Routes (28%)
- Database Models (23%)
- Authentication (19%)
- Business Logic (18%)
- Test Suites (12%)

Export as HTML:

User: "Export visualization as HTML"

Result: Standalone HTML file with:
- Interactive hover, zoom, pan
- Click clusters to highlight
- Modern gradient UI
- Works offline

Understanding the Visualization

Colors and Clusters:

• Each color represents a semantic cluster (module/functionality)
• Points close together = similar in meaning
• Distance reflects semantic similarity
• Outliers indicate unique/specialized code

Common Cluster Patterns:

• Blue: Frontend/UI components
• Orange: API endpoints and routes
• Green: Database models and queries
• Red: Authentication and security
• Purple: Tests and validation
• Gray: Utilities and helpers

Use Cases

1. 🏗️ Architecture Understanding

   • Visualize to see module boundaries
   • Identify tightly coupled code
   • Find opportunities for refactoring

2. 🔍 Code Discovery

   • Locate related functionality visually
   • Find all code touching a feature
   • Discover cross-cutting concerns

3. 🐛 Search Debugging

   • Understand why results were retrieved
   • See semantic relationships
   • Refine queries based on visualization

4. 👥 Team Onboarding

   • Export HTML for new developers
   • Visual guide to codebase structure
   • Interactive exploration tool

5. ✅ Refactoring Validation

   • Visualize before/after refactoring
   • Verify improved code organization
   • Track architecture evolution

Performance

Collection Size	Processing Time	Recommended maxVectors
Small (<500 vectors)	~1s	500
Medium (500-2K)	~4s	1000
Large (2K-10K)	~15s	2000
Very Large (>10K)	~30s	3000

Tips:

• Use 2D for faster processing (40% faster than 3D)
• Limit maxVectors for large codebases
• Export HTML for offline exploration

📖 Learn More

For detailed documentation including:

• Complete tool reference
• Interpretation guide
• Technical details (UMAP, clustering)
• Troubleshooting
• Best practices
• Advanced use cases

See: Vector Visualization Guide

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🎯 Prompt Enhancement (Optional)

TL;DR: Prompt enhancement is a transparent background tool that automatically improves search quality. Just ask naturally - no need to mention "enhance" in your prompts.

Quick Overview

When enabled (PROMPT_ENHANCEMENT=true), the AI automatically:

1. Enhances your search query with codebase context
2. Searches with the improved query
3. Continues with your original request (implement, fix, explain, etc.)

Good Prompts ✅

✅ "Find authentication logic and add 2FA support"
✅ "Locate payment flow and fix the timeout issue"
✅ "Search for profile feature and add bio field"

Why these work: Clear goal (find + action) → AI knows what to do

Bad Prompts ❌

❌ "Enhance and search for authentication"
❌ "Use prompt enhancement to find profile"

Why these fail: No clear action → AI stops after search

Key Principle

Prompt enhancement is invisible infrastructure.

Just tell the AI what you want to accomplish. It will automatically use enhancement to improve search quality behind the scenes.

Think of it like autocomplete: You don't say "use autocomplete" - you just type and it helps automatically.

📖 Learn More

For detailed guide including:

• Technical details and architecture
• Configuration options
• Real-world examples (TypeScript, Python, Dart, etc.)
• Performance tips and optimization
• Troubleshooting and FAQ
• Advanced use cases

See: Prompt Enhancement Guide

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🎛️ Configuration

Required Variables

{
  "env": {
    "REPO_PATH": "/Users/you/Projects/myapp",
    "GEMINI_API_KEY": "AIzaSyC...",
    "QDRANT_URL": "https://xxx.gcp.cloud.qdrant.io:6333",
    "QDRANT_API_KEY": "eyJhbGci..."
  }
}

Optional Variables

{
  "env": {
    "QDRANT_COLLECTION": "my_project",
    "WATCH_MODE": "true",
    "BATCH_SIZE": "50",
    "EMBEDDING_MODEL": "text-embedding-004",
    "PROMPT_ENHANCEMENT": "true"
  }
}

📖 Full configuration guide: Setup Guide

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🌍 Supported Languages

Python • TypeScript • JavaScript • Dart • Go • Rust • Java • Kotlin • Swift • Ruby • PHP • C • C++ • C# • Shell • SQL • HTML • CSS

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📊 Performance

Metric	Value
Indexing Speed	~25 files/min
Search Latency	<100ms
Incremental Savings	90%+ time reduction
Parallel Processing	25 chunks/sec

📖 Performance details: Main Documentation

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🐛 Troubleshooting

Server not appearing?

1. Check Copilot Chat → Settings → MCP Servers → Show Output
2. Verify all 4 env variables are set
3. Ensure REPO_PATH is absolute path

Can't connect to Qdrant?

curl -H "api-key: YOUR_KEY" \
  https://YOUR_CLUSTER.gcp.cloud.qdrant.io:6333/collections

Indexing too slow?

• Large repos take 5-10 minutes initially
• Subsequent runs only index changed files (90%+ faster)

📖 More troubleshooting: Main Documentation

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📁 Project Structure

mcp-codebase-index/
├── docs/                    # All documentation
│   ├── README.md           # Main documentation
│   ├── SETUP.md            # Setup guide
│   ├── CHANGELOG.md        # Version history
│   ├── NAVIGATION.md       # Navigation guide
│   ├── guides/             # Detailed guides
│   └── planning/           # Development planning
│
├── src/                     # Source code
│   ├── core/               # Core business logic
│   ├── storage/            # Data persistence
│   ├── enhancement/        # Prompt enhancement
│   ├── visualization/      # Vector visualization
│   ├── mcp/                # MCP server
│   │   ├── server.ts      # Server orchestration (1237 lines)
│   │   ├── handlers/      # Modular handlers (1045 lines)
│   │   ├── templates/     # HTML templates
│   │   └── types/         # Handler types
│   ├── types/              # Type definitions
│   └── index.ts            # Entry point
│
├── config/                  # Configuration files
├── .data/                   # Runtime data (gitignored)
├── package.json
└── README.md               # This file

📖 Detailed structure: Project Structure | Source Code Structure

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🔧 Development

Build

npm run build

Run Locally

npm run dev

Test

npm test

📖 Development guide: Source Code Structure

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🤝 Contributing

Contributions welcome! Check out:

• Improvement Plan - Roadmap
• Issues - Detailed feature docs
• Source Code - Code structure

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📄 License

MIT © NgoTaiCo

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: ngotaico.flutter@gmail.com

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