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Advanced codebase indexing and semantic search MCP server

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    Readme

    Intelligent Context MCP

    A comprehensive Model Context Protocol (MCP) server by Wildcard Corporation that provides advanced codebase indexing and semantic search with slash commands and natural language interface for Claude Code.

    NPM Version License: MIT TypeScript

    🎯 Features

    🚀 Dual Interface Options

    • Slash Commands: /index, /search, /status, /clear, /context, /deps, /help
    • Natural Language: "Find authentication functions", "Index my codebase"
    • Traditional MCP Tools: Direct tool calls for programmatic access

    🧠 Advanced Intelligence

    • Tree-sitter semantic chunking - AST-based code chunking creating meaningful units (complete functions, classes, interfaces)
    • Smart token limit handling - Automatic content truncation with intelligent boundary detection for API limits
    • Content quality filtering - Excludes test files, generated code, and low-quality content
    • Dependency graph analysis - Cross-file relationship mapping and context expansion
    • Incremental indexing - Only re-indexes changed files and dependencies
    • Multi-strategy search - Semantic, hybrid, BM25, and structural search options

    🤖 AI-Powered Features

    • Result Reranking - Jina reranker-v2 optimizes relevance scoring
    • Local BM25 Search - SQLite full-text search for exact keyword matching
    • Hybrid Fusion - Combines vector similarity with BM25 for best results

    🔧 Production Ready

    • Real API integration - Jina AI embeddings + Turbopuffer vector storage
    • Multi-language support - 30+ programming languages with Tree-sitter parsing
    • Error handling - Graceful degradation and comprehensive error reporting
    • Performance optimized - Batch processing and intelligent caching

    🚀 Installation & Setup

    1. Install Dependencies

    npm install
    npm run build

    2. Get API Keys

    Required APIs:

    • Jina AI: Get your API key from Jina AI - For embeddings and reranking
    • Turbopuffer: Get your API key from Turbopuffer - For vector storage

    3. Add to Claude Code

    # Install via npm
    npm install -g @wildcard-corp/intelligent-context-mcp
    
    # Add to Claude Code
    claude mcp add intelligent-context \\
      -e JINA_API_KEY=your-jina-key \\
      -e TURBOPUFFER_API_KEY=your-turbopuffer-key \\
      -- npx @wildcard-corp/intelligent-context-mcp

    🎮 Usage

    Execute commands using slash syntax:

    # Index your codebase with intelligent chunking
    /index /path/to/your/project
    
    # Search with semantic understanding
    /search authentication implementation
    /search user registration flow  
    /search database connection setup
    
    # Check indexing status
    /status
    /status /specific/project/path
    
    # Get focused context for specific files or symbols
    /context src/auth.js --with-deps
    /context UserService --window=10
    
    # Analyze dependencies
    /deps src/user.js --reverse
    /deps AuthController --graph
    
    # Clear index data
    /clear --confirm
    /clear /path/to/project --confirm
    
    # Get help
    /help
    /help search

    Natural Language Interface

    Use conversational queries:

    "Find all authentication functions"
    "Show me the user registration flow"
    "Index my codebase at /path/to/project"
    "What's the status of my index?"
    "Get context for the login function"

    Traditional MCP Tools

    Direct tool calls for programmatic access:

    • execute_slash_command - Execute any slash command
    • natural_language_query - Process natural language queries
    • index_codebase_intelligent - Direct indexing (legacy)
    • search_with_intelligence - Direct search (legacy)

    ⚙️ Configuration

    Environment Variables

    Variable Required Description
    JINA_API_KEY Your Jina AI API key for embeddings
    TURBOPUFFER_API_KEY Your Turbopuffer API key for vector storage
    LOG_LEVEL Logging level: debug, info, warn, error (default: info)
    CODEX_CONTEXT_DATA_DIR Data storage directory (default: ~/.codex-context)

    API Integration Details

    • Jina AI Embeddings: Uses jina-embeddings-v3 model with 1024 dimensions
    • Turbopuffer Storage: Vector storage with cosine distance similarity
    • Tree-sitter Parsing: AST parsing for accurate symbol extraction

    🏗️ Architecture

    Enhanced Two-Layer Design

    graph TD
        A[Claude Code CLI] --> B[Enhanced MCP Interface]
        B --> C{Interface Type}
        C -->|Slash Commands| D[Command Parser]
        C -->|Natural Language| E[NL Interpreter] 
        C -->|Direct Tools| F[Tool Handler]
        D --> G[Integration Bridge]
        E --> G
        F --> G
        G --> H[Core Components]
        H --> I[IndexingOrchestrator]
        H --> J[SemanticSearchEngine]
        H --> K[TreeSitterSymbolExtractor]
        I --> L[Real APIs]
        J --> L
        K --> L

    Core Components

    1. Enhanced MCP Interface (src/enhanced-mcp.ts)

      • Primary interface with slash commands and natural language
      • MCP protocol handling and state management
      • Command registry with extensible architecture
    2. Integration Bridge (src/standalone-mcp-integration.ts)

      • Connects interface layer with core components
      • Real API integration (Jina AI + Turbopuffer)
      • Data format conversion and error handling
    3. Core Intelligence Components (src/core/)

      • IndexingOrchestrator: Advanced codebase indexing
      • SemanticSearchEngine: Multi-strategy intelligent search
      • TreeSitterSymbolExtractor: AST-based symbol extraction
      • ContentFilterProvider: Quality filtering and noise reduction
      • IncrementalIndexer: Efficient change detection and updates

    📊 Performance & Quality

    Indexing Performance

    • Symbol-boundary chunking: No arbitrary line splits
    • Content filtering: Excludes ~40% of noise (tests, configs, generated code)
    • Incremental updates: Only processes changed files
    • Batch processing: Optimized API calls and vector uploads

    Search Quality

    • Multi-stage ranking: Vector similarity + optional reranking
    • Dependency expansion: Finds related code across file boundaries
    • Context windows: Configurable context around matches
    • Symbol awareness: Understands code structure and relationships

    🔍 Examples

    Indexing a React Project

    /index /path/to/react-app --force
    # ✅ Successfully indexed 1,247 files into 3,821 intelligent chunks
    # 🔍 Ready for intelligent search with `/search <query>`

    Finding Authentication Code

    /search user authentication login
    # 🔍 Found 15 results (234ms):
    # 
    # **src/auth/AuthService.ts:45-67** (0.923)
    # ```typescript
    # async authenticateUser(credentials: LoginCredentials): Promise<AuthResult> {
    #   const user = await this.userRepository.findByEmail(credentials.email);
    #   if (!user || !await this.verifyPassword(credentials.password, user.hashedPassword)) {
    #     throw new UnauthorizedError('Invalid credentials');
    #   }
    #   return this.generateAuthTokens(user);
    # }
    # ```

    Getting Context with Dependencies

    /context src/auth/AuthService.ts --with-deps
    # 📋 Context for AuthService with dependencies:
    # - Depends on: UserRepository, TokenService, PasswordHasher
    # - Used by: LoginController, SignupController, AuthMiddleware
    # - Related symbols: authenticateUser, verifyPassword, generateTokens

    🎉 Benefits

    For Developers

    • Intuitive interface with familiar slash commands
    • Natural language queries for non-technical stakeholders
    • Comprehensive context with dependency awareness
    • Fast search with semantic understanding

    For Teams

    • Consistent indexing with quality filtering
    • Cross-file understanding via dependency analysis
    • Incremental updates for active development
    • Production ready with robust error handling

    🚀 Advanced Usage

    Custom Search Strategies

    # Semantic search for concepts
    /search "how does authentication work" --type=semantic
    
    # Structural search for patterns  
    /search "function.*login.*password" --type=structural
    
    # Hybrid search combining both
    /search authentication --type=hybrid

    Dependency Analysis

    # Find all files that depend on AuthService
    /deps AuthService --reverse
    
    # Show dependency graph for authentication module
    /deps src/auth/ --graph --depth=3

    Context Windows

    # Get 20 lines of context around matches
    /search database connection --window=20
    
    # Focus on specific symbol types
    /context UserService --focus=functions --with-deps

    🛠️ Development

    Project Structure

    ├── src/                    # Source code
    │   ├── core/              # Core indexing and search logic
    │   ├── services/          # Search and utility services  
    │   ├── utils/             # Utilities (Logger, FileUtils, etc.)
    │   └── types/             # TypeScript type definitions
    ├── tests/                 # Essential test files
    ├── scripts/               # Utility scripts
    └── dist/                  # Compiled JavaScript output

    Running Tests

    # Set environment variables
    export JINA_API_KEY="your_jina_api_key"
    export TURBOPUFFER_API_KEY="your_turbopuffer_key"
    
    # Test semantic chunking quality
    node tests/final-chunking-validation.mjs
    
    # Test search integration end-to-end
    node tests/test-improved-search-quality.mjs
    
    # Test search result quality directly
    node tests/test-search-results-direct.mjs
    
    # Test MCP server functionality  
    node tests/test-mcp-tools-directly.mjs

    Key Components

    • TreeSitterChunkExtractor - AST-based semantic code chunking
    • HybridSearchService - Vector + BM25 search fusion
    • StandaloneCodexMcp - Main MCP server integration
    • IndexingOrchestrator - Coordinates the indexing pipeline

    🎯 What Makes This Special

    Unlike simple text search tools, this MCP provides:

    1. True code understanding via AST parsing and symbol extraction
    2. Quality-focused indexing that filters out noise automatically
    3. Dependency-aware search that finds related code across files
    4. Multiple interfaces to suit different user preferences
    5. Production deployment with real API integrations
    6. Incremental efficiency for large, active codebases

    Perfect for teams using Claude Code who need intelligent codebase exploration and context-aware development assistance.


    Ready to enhance your codebase exploration with Claude Code! 🚀