Building CodeSage: A MCP-First Code Discovery Platform

This is a submission for the Algolia MCP Server Challenge

What I Built

CodeSage is an AI-powered code discovery platform built entirely around the Model Context Protocol (MCP). It transforms GitHub repositories into AI-searchable knowledge bases, enabling natural language exploration of codebases through Claude Desktop and other MCP-compatible AI clients.

Demo

GitHub Repository: CodeSage MCP-First Code Discovery

ntanwir10 / codesage-algolia-challenge

AI-powered code discovery through natural language - Built entirely around the Model Context Protocol (MCP) for seamless integration with Claude Desktop and other AI clients

CodeSage - MCP-First Code Discovery

🚀 AI-powered code discovery through natural language - Built entirely around the Model Context Protocol (MCP) for seamless integration with Claude Desktop and other AI clients.

CodeSage - MCP-First Code Discovery - The only code discovery tool built entirely around the Model Context Protocol for seamless AI integration.

Built for the Algolia MCP Server Challenge 🏆

Competing in the Backend Data Optimization and Ultimate User Experience categories with our innovative MCP-first approach to code discovery.

🎯 What is CodeSage?

CodeSage - MCP-First Code Discovery transforms GitHub repositories into AI-searchable knowledge bases through the Model Context Protocol. Submit a repository URL, and within minutes your AI assistant can discover functions, understand architecture, and answer complex questions about the codebase through natural language - all via MCP integration.

🎯 Final User Experience

1. User submits: github.com/facebook/react
2. System processes: GitHub → Parser → Algolia
3. User opens

…

View on GitHub

Key Innovation: MCP-First Architecture

CodeSage is the only code discovery tool built entirely around MCP standards. Unlike traditional code search tools that require direct API integration, CodeSage leverages the Model Context Protocol to provide seamless AI integration, eliminating the need for direct AI API calls.

📊 Complete System Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│                              CODESAGE ARCHITECTURE                          │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌─────────────┐    ┌──────────────┐    ┌─────────────┐    ┌─────────────┐ │
│  │   Frontend  │───▶│   Backend    │───▶│   Algolia   │    │   GitHub    │ │
│  │   (Simple)  │    │ (Processing) │    │  (Search)   │◀───│     API     │ │
│  └─────────────┘    └──────────────┘    └─────────────┘    └─────────────┘ │
│                              │                                             │
│                              ▼                                             │
│                     ┌──────────────┐    ┌─────────────┐                   │
│                     │     MCP      │───▶│   Claude    │                   │
│                     │   Protocol   │    │  Desktop    │                   │
│                     └──────────────┘    └─────────────┘                   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

Key Architecture Flows:

• Repository Management: Frontend ↔ Backend
• Code Discovery: Claude Desktop ↔ MCP ↔ Backend ↔ Algolia
• No Direct Integration: Frontend never talks to Algolia or MCP

🔄 Processing Pipeline

1. Repository Submission Flow

User Input → Frontend → POST /repositories/ → Backend
    ↓
Database Record (status: pending)
    ↓
Background Processing Triggered
    ↓
GitHub API Integration
    ↓
File Parsing & Analysis
    ↓
Algolia Indexing
    ↓
Status Update (status: completed)

2. Code Discovery Flow

Claude Desktop User Query
    ↓
MCP Protocol Call
    ↓
POST /api/v1/ai/mcp/tools/call
    ↓
MCP Tool Execution
    ↓
Algolia Search Query
    ↓
Search Results
    ↓
Formatted Response
    ↓
Claude Desktop Display

Core Features:

• Repository Processing: Automatic ingestion and parsing of GitHub repositories
• Algolia-Powered Search: Fast, semantic code search with intelligent indexing
• MCP Tools: Natural language code discovery through Claude Desktop
• Real-time Processing: Live status updates and WebSocket integration
• Multi-language Support: Parses functions, classes, and imports across languages

How I Utilized the Algolia MCP Server

1. MCP-First Architecture Design

CodeSage is built entirely around the Model Context Protocol, making it unique among code discovery tools. The architecture follows this flow:

User Query → Claude Desktop → MCP Protocol → CodeSage Backend → Algolia Search → Formatted Results

2. Algolia Integration for Code Search

I implemented a comprehensive Algolia service that:

• Indexes Code Entities: Functions, classes, variables, and imports are parsed and indexed with semantic metadata
• Optimized Search Settings: Configured searchable attributes, faceting, and custom ranking for code-specific relevance
• Multi-language Support: Handles Python, JavaScript, TypeScript, and other languages
• Repository Isolation: Each repository gets its own search context

3. MCP Tools Implementation

I created five core MCP tools that leverage Algolia's search capabilities:

• search_code: Natural language code search across repositories
• analyze_repository: Repository overview and architectural insights
• explore_functions: Function discovery and relationship mapping
• explain_code: Detailed code explanations and documentation
• find_patterns: Pattern detection for security, performance, and architecture

4. Real-world Usage Example

# User submits repository
POST /api/v1/repositories/
{
  "url": "https://github.com/facebook/react"
}

# User asks Claude Desktop
"Show me React's rendering lifecycle functions"

# Claude uses MCP tool
POST /api/v1/ai/mcp/tools/call
{
  "tool_name": "search_code",
  "arguments": {
    "query": "rendering lifecycle",
    "repository": "react"
  }
}

# Algolia returns relevant functions
# Claude provides AI-powered analysis

5. MCP Tools Available

• search_code - Natural language code search across repositories
• analyze_repository - Repository overview and architectural insights
• explore_functions - Function discovery and relationship mapping
• explain_code - Detailed code explanations and documentation
• find_patterns - Pattern detection for security, performance, architecture

6. Technical Implementation

🏗 System Components

Backend (FastAPI)

Location: backend/app/

Core Services:

• repository_service.py - Repository CRUD and processing logic
• mcp_server.py - MCP protocol implementation
• ai_service.py - MCP tools implementation
• algolia_service.py - Search indexing and querying
• security_service.py - Rate limiting and validation

Database Models:

• Repository - Repository metadata and status
• CodeFile - Individual file records
• CodeEntity - Functions, classes, imports extracted from files

API Endpoints:

# Repository Management
GET    /api/v1/repositories/        # List repositories
POST   /api/v1/repositories/        # Create repository  
GET    /api/v1/repositories/{id}    # Get repository
DELETE /api/v1/repositories/{id}    # Delete repository

# MCP Protocol
GET    /api/v1/ai/mcp/capabilities     # MCP server capabilities
GET    /api/v1/ai/mcp/tools           # List MCP tools
POST   /api/v1/ai/mcp/tools/call      # Execute MCP tool
GET    /api/v1/ai/mcp/resources/read  # Read MCP resource

Frontend (React + TypeScript)

Location: frontend/src/

Purpose: Simple repository management interface

• Repository submission form
• Repository list with status
• Basic CRUD operations
• No complex search UI - AI discovery happens through Claude Desktop

Key Components:

• Repository form with GitHub URL validation
• Real-time status tracking via WebSocket
• Modern UI with TailwindCSS

Tech Stack

Backend Stack:

• FastAPI for MCP server implementation
• SQLAlchemy for data persistence
• Algolia for search indexing and querying
• WebSocket for real-time status updates

Frontend Stack:

• React 18 with TypeScript
• TailwindCSS for modern UI
• Real-time status tracking

MCP Integration:

• Custom MCP server implementation
• Tool-based architecture for extensibility
• Resource-based data access patterns

Key Takeaways

Development Process

Building CodeSage taught me several valuable lessons about MCP-first architecture:

1. MCP Standards Matter: Building around MCP from the ground up creates a more flexible and extensible system than retrofitting existing tools
2. Tool Design is Critical: Well-designed MCP tools can provide powerful AI capabilities without requiring direct AI API integration
3. Search Integration is Key: Algolia's semantic search capabilities perfectly complement MCP's natural language interface

Challenges Faced

1. MCP Protocol Complexity: Understanding and implementing the Model Context Protocol required a deep dive into the specification
2. Search Optimization: Configuring Algolia for code-specific search patterns required extensive testing and tuning
3. Real-time Processing: Implementing background processing with status updates required careful WebSocket integration
4. Error Handling: MCP tools need robust error handling to provide meaningful feedback to AI clients

What I Learned

1. MCP-First Design: Building around MCP standards from the start creates more AI-friendly applications
2. Search Architecture: Algolia's faceting and filtering capabilities are perfect for code discovery
3. Tool Abstraction: Well-designed MCP tools can abstract complex operations into simple natural language interfaces
4. Performance Optimization: Code search requires careful indexing strategies and query optimization

Technical Innovations

1. MCP-First Code Discovery: The only code discovery tool built entirely around MCP standards
2. Algolia-MCP Integration: Seamless integration between Algolia's search capabilities and MCP's tool system
3. Natural Language Code Exploration: Users can ask complex questions about codebases through Claude Desktop
4. Repository Processing Pipeline: Automated ingestion, parsing, and indexing of GitHub repositories

Future Possibilities

CodeSage demonstrates the potential for MCP-first applications in various domains:

• Documentation Discovery: Similar approach for technical documentation
• API Exploration: Natural language API discovery and testing
• Security Analysis: AI-powered security pattern detection
• Code Review: Automated code review through MCP tools

The combination of Algolia's powerful search capabilities with MCP's natural language interface opens up exciting possibilities for AI-powered development tools.

🚀 Impact

CodeSage represents a fundamental shift in how developers interact with codebases. As the first MCP-first code discovery platform, it demonstrates:

• AI-Native Design: Building around MCP standards creates more powerful AI integration
• Natural Language Future: Developers can ask complex questions about codebases in plain English
• Search + AI Synergy: Algolia's semantic search combined with MCP's natural language interface

This project has broader implications for the AI tool ecosystem, showing how to build applications that work seamlessly with Claude Desktop and other MCP clients. The architecture can be applied to documentation discovery, API exploration, security analysis, and knowledge management.

CodeSage proves that MCP-first applications can revolutionize how we interact with complex data through natural language, opening new possibilities for AI-powered development tools.

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Thanks for participating! 🚀

This project represents a new paradigm in AI-powered development tools, one where the Model Context Protocol enables seamless integration between powerful search engines, such as Algolia, and AI assistants, like Claude Desktop.