ViperJuice/Code-Index-MCP

Code-Index-MCP (Local-first Code Indexer)

Modular, extensible local-first code indexer designed to enhance Claude Code and other LLMs with deep code understanding capabilities. Built on the Model Context Protocol (MCP) for seamless integration with AI assistants.

Implementation Status

Current completion: 100% (Production-ready with comprehensive validation completed)
System complexity: 5/5 (High - 136k lines, semantic search, distributed architecture)
Production ready: Yes - All systems validated, sub-100ms query performance, complete documentation

🎯 Key Features

• 🚀 Local-First Architecture: All indexing happens locally for speed and privacy
• 📂 Local Index Storage: All indexes stored at .indexes/ (relative to MCP server)
• 🔌 Plugin-Based Design: Easily extensible with language-specific plugins
• 🔍 48-Language Support: Complete tree-sitter integration with semantic search
• ⚡ Real-Time Updates: File system monitoring for instant index updates
• 🧠 Semantic Search: AI-powered code search with Voyage AI embeddings
• 📊 Rich Code Intelligence: Symbol resolution, type inference, dependency tracking
• 🚀 Enhanced Performance: Sub-100ms queries with timeout protection and BM25 bypass
• 🔄 Git Synchronization: Automatic index updates tracking repository changes
• 📦 Portable Index Management: Zero-cost index sharing via GitHub Artifacts
• 🔄 Automatic Index Sync: Pull indexes on clone, push on changes
• 🎯 Smart Result Reranking: Multi-strategy reranking for improved relevance
• 🔒 Security-Aware Export: Automatic filtering of sensitive files from shared indexes
• 🔍 Hybrid Search: BM25 + semantic search with configurable fusion
• 🔐 Index Everything Locally: Search .env files and secrets on your machine
• 🚫 Smart Filtering on Share: .gitignore and .mcp-index-ignore patterns applied only during export
• 🌐 Multi-Language Indexing: Index entire repositories with mixed languages

🏗️ Architecture

The Code-Index-MCP follows a modular, plugin-based architecture designed for extensibility and performance:

System Layers

1. 🌐 System Context (Level 1)

   • Developer interacts with Claude Code or other LLMs
   • MCP protocol provides standardized tool interface
   • Local-first processing with optional cloud features
   • Performance SLAs: <100ms symbol lookup, <500ms search

2. 📦 Container Architecture (Level 2)

   ┌─────────────────┐     ┌──────────────┐     ┌─────────────┐
   │   API Gateway   │────▶│  Dispatcher  │────▶│   Plugins   │
   │   (FastAPI)     │     │              │     │ (Language)  │
   └─────────────────┘     └──────────────┘     └─────────────┘
          │                        │                     │
          ▼                        ▼                     ▼
   ┌─────────────────┐     ┌──────────────┐     ┌─────────────┐
   │  Local Index    │     │ File Watcher │     │  Embedding  │
   │  (SQLite+FTS5)  │     │  (Watchdog)  │     │   Service   │
   └─────────────────┘     └──────────────┘     └─────────────┘
   

3. 🔧 Component Details (Level 3)

   • Gateway Controller: RESTful API endpoints
   • Dispatcher Core: Plugin routing and lifecycle
   • Plugin Base: Standard interface for all plugins
   • Language Plugins: Specialized parsers and analyzers
   • Index Manager: SQLite with FTS5 for fast searches
   • Watcher Service: Real-time file monitoring

📁 Project Structure

The project follows a clean, organized structure. See for detailed layout.

Key directories:

• mcp_server/ - Core MCP server implementation
• scripts/ - Development and utility scripts
• tests/ - Comprehensive test suite with fixtures
• docs/ - Documentation and guides
• architecture/ - System design and diagrams
• docker/ - Docker configurations and compose files
• data/ - Database files and indexes
• logs/ - Application and test logs
• reports/ - Generated performance reports and analysis
• analysis_archive/ - Historical analysis and archived research

🛠️ Language Support

✅ Fully Supported Languages (46+ Total)

Production-Ready Features:

• Dynamic Plugin Loading: Languages are loaded on-demand for optimal performance
• Tree-sitter Parsing: Accurate AST-based symbol extraction with language-specific queries
• Query Caching: Improved performance with cached tree-sitter queries
• Semantic Search: Optional AI-powered code search (when Qdrant is available)
• Cross-Language Search: Find symbols and patterns across all supported languages

Language Categories:

Implementation Status: Production-Ready - All languages supported via the enhanced dispatcher with:

• ✅ Dynamic plugin loading (lazy initialization)
• ✅ Robust error handling and fallback mechanisms
• ✅ Path resolution for complex project structures
• ✅ Graceful degradation when external services unavailable

🚀 Quick Start

🎯 Automatic Setup for Claude Code/Desktop (Recommended)

# Auto-configures MCP for your environment
./scripts/setup-mcp-json.sh

# Or interactive mode
./scripts/setup-mcp-json.sh --interactive

This automatically detects your environment and creates the appropriate .mcp.json configuration.

🐳 Docker Setup by Environment

Option 1: Basic Search (No API Keys) - 2 Minutes

# Install MCP Index with Docker
curl -sSL https://raw.githubusercontent.com/Code-Index-MCP/main/scripts/install-mcp-docker.sh | bash

# Index your current directory
docker run -it -v $(pwd):/workspace ghcr.io/code-index-mcp/mcp-index:minimal

Option 2: AI-Powered Search

# Set your API key (get one at https://voyageai.com)
export VOYAGE_AI_API_KEY=your-key

# Run with semantic search
docker run -it -v $(pwd):/workspace -e VOYAGE_AI_API_KEY ghcr.io/code-index-mcp/mcp-index:standard

💻 Environment-Specific Setup

🪟 Windows (Native)

# PowerShell
.\scripts\setup-mcp-json.ps1

# Or manually with Docker Desktop
docker run -it -v ${PWD}:/workspace ghcr.io/code-index-mcp/mcp-index:minimal

🍎 macOS

# Install Docker Desktop or use Homebrew
brew install --cask docker

# Run setup
./scripts/setup-mcp-json.sh

🐧 Linux

# Install Docker (no Desktop needed)
curl -fsSL https://get.docker.com | sh

# Run setup
./scripts/setup-mcp-json.sh

🔄 WSL2 (Windows Subsystem for Linux)

# With Docker Desktop integration
./scripts/setup-mcp-json.sh  # Auto-detects WSL+Docker

# Without Docker Desktop
cp .mcp.json.templates/native.json .mcp.json
pip install -e .

📦 Nested Containers (Dev Containers)

# For VS Code/Cursor dev containers
# Option 1: Use native Python (already in container)
cp .mcp.json.templates/native.json .mcp.json

# Option 2: Use Docker sidecar (avoids dependency conflicts)
docker-compose -f docker/compose/development/docker-compose.mcp-sidecar.yml up -d
cp .mcp.json.templates/docker-sidecar.json .mcp.json

📋 MCP.json Configuration Examples

The setup script creates the appropriate .mcp.json for your environment. Manual examples:

Native Python (Dev Container/Local)

{
  "mcpServers": {
    "code-index-native": {
      "command": "python",
      "args": ["scripts/cli/mcp_server_cli.py"],
      "cwd": "${workspace}"
    }
  }
}

Docker (Windows/Mac/Linux)

{
  "mcpServers": {
    "code-index-docker": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "${workspace}:/workspace",
        "ghcr.io/code-index-mcp/mcp-index:minimal"
      ]
    }
  }
}

💰 Costs & Features

🚀 Quickstart (Python)

Prerequisites

• Python 3.8+
• Git
• Docker (optional, for architecture diagrams)

Installation

Option 1: Quick Start with Pre-built Index

Download a pre-built index from our releases to get started immediately:

# Download latest release
python scripts/download-release.py --latest

# Or download a specific version
python scripts/download-release.py --tag v2024.01.15

Option 2: Build from Source

1. Clone the repository

   git clone https://github.com/yourusername/Code-Index-MCP.git
   cd Code-Index-MCP
   

2. Install dependencies

   # Create virtual environment
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   
   # Install requirements
   pip install -r requirements.txt
   

3. Build the index (or download pre-built)

   # Build index for current directory with full support (SQL + Semantic)
   python scripts/index_repositories.py --mode full
   
   # Or use specific modes:
   # SQL-only (fast, no API key needed):
   python scripts/index_repositories.py --mode sql
   
   # Semantic-only (requires VOYAGE_AI_API_KEY):
   python scripts/index_repositories.py --mode semantic
   
   # Or download from GitHub artifacts (if available)
   python scripts/index-artifact-download-v2.py --latest
   

4. Start the server

   # Start the MCP server
   uvicorn mcp_server.gateway:app --reload --host 0.0.0.0 --port 8000
   

5. Test the API

   # Check server status
   curl http://localhost:8000/status
   
   # Search for code
   curl -X POST http://localhost:8000/search \
     -H "Content-Type: application/json" \
     -d '{"query": "def parse"}'
   

🔧 Configuration

Create a .env file for configuration:

# Optional: Voyage AI for semantic search
VOYAGE_AI_API_KEY=your_api_key_here

# Server settings
MCP_SERVER_HOST=0.0.0.0
MCP_SERVER_PORT=8000
MCP_LOG_LEVEL=INFO

# Workspace settings
MCP_WORKSPACE_ROOT=.
MCP_MAX_FILE_SIZE=10485760  # 10MB

# GitHub Artifact Sync (privacy settings)
MCP_ARTIFACT_SYNC=false  # Set to true to enable
AUTO_UPLOAD=false        # Auto-upload on changes
AUTO_DOWNLOAD=true       # Auto-download on clone

🔐 Privacy & GitHub Artifact Sync

Control how your code index is shared:

// .mcp-index.json
{
  "github_artifacts": {
    "enabled": false,        // Disable sync entirely
    "auto_upload": false,    // Manual upload only
    "auto_download": true,   // Still get team indexes
    "exclude_patterns": [    // Additional exclusions
      "internal/*",
      "proprietary/*"
    ]
  }
}

Privacy Features:

• Indexes filtered by .gitignore automatically
• Additional patterns via .mcp-index-ignore
• Audit logs show what was excluded
• Sync disabled by default in Docker minimal version

🆕 Advanced Features

Search Result Reranking

The system includes multiple reranking strategies to improve search relevance:

# Configure reranking in your searches
from mcp_server.indexer.reranker import RerankConfig, TFIDFReranker

config = RerankConfig(
    enabled=True,
    reranker=TFIDFReranker(),  # Or CohereReranker(), CrossEncoderReranker()
    top_k=20
)

# Search with reranking
results = await search_engine.search(query, rerank_config=config)

Available Rerankers:

• TF-IDF: Fast, local reranking using term frequency
• Cohere: Cloud-based neural reranking (requires API key)
• Cross-Encoder: Local transformer-based reranking
• Hybrid: Combines multiple rerankers with fallback

Security-Aware Index Sharing

Prevent accidental sharing of sensitive files:

# Analyze current index for security issues
python scripts/utilities/analyze_gitignore_security.py

# Create secure index export (filters gitignored files)
python scripts/utilities/secure_index_export.py

# The secure export will:
# - Exclude all gitignored files
# - Remove sensitive patterns (*.env, *.key, etc.)
# - Create audit logs of excluded files

BM25 Hybrid Search

Combines traditional full-text search with semantic search:

# The system automatically uses hybrid search when available
# Configure weights in settings:
HYBRID_SEARCH_BM25_WEIGHT=0.3
HYBRID_SEARCH_SEMANTIC_WEIGHT=0.5
HYBRID_SEARCH_FUZZY_WEIGHT=0.2

🔧 Dispatcher Configuration

Enhanced Dispatcher (Default)

The enhanced dispatcher includes timeout protection and automatic fallback:

from mcp_server.dispatcher.dispatcher_enhanced import EnhancedDispatcher
from mcp_server.storage.sqlite_store import SQLiteStore

store = SQLiteStore(".indexes/YOUR_REPO_ID/current.db")
dispatcher = EnhancedDispatcher(
    sqlite_store=store,
    semantic_search_enabled=True,  # Enable if Qdrant available
    lazy_load=True,               # Load plugins on-demand
    use_plugin_factory=True       # Use dynamic plugin loading
)

# Search with automatic optimization
results = list(dispatcher.search("your query", limit=10))

Simple Dispatcher (Lightweight Alternative)

For maximum performance with BM25-only search:

from mcp_server.dispatcher.simple_dispatcher import create_simple_dispatcher

# Ultra-fast BM25 search without plugin overhead
dispatcher = create_simple_dispatcher(".indexes/YOUR_REPO_ID/current.db")
results = list(dispatcher.search("your query", limit=10))

Configuration Options

Configure dispatcher behavior via environment variables:

# Dispatcher settings
MCP_DISPATCHER_TIMEOUT=5          # Plugin loading timeout (seconds)
MCP_USE_SIMPLE_DISPATCHER=false   # Use simple dispatcher
MCP_PLUGIN_LAZY_LOAD=true        # Load plugins on-demand

# Performance tuning
MCP_BM25_BYPASS_ENABLED=true     # Enable direct BM25 bypass
MCP_MAX_PLUGIN_MEMORY=1024       # Max memory for plugins (MB)

🗂️ Index Management

Centralized Index Storage

All indexes are now stored centrally at .indexes/ (relative to the MCP project) for better organization and to prevent accidental commits:

.indexes/
├── {repo_hash}/              # Unique hash for each repository
│   ├── main_abc123.db        # Index for main branch at commit abc123
│   ├── main_abc123.metadata.json
│   └── current.db -> main_abc123.db  # Symlink to active index
├── qdrant/                   # Semantic search embeddings
│   └── main.qdrant/          # Centralized Qdrant database

Benefits:

• Indexes never accidentally committed to git
• Reusable across multiple clones of same repository
• Clear separation between code and indexes
• Automatic discovery based on git remote

Migration: For existing repositories with local indexes:

python scripts/move_indexes_to_central.py

For This Repository

This project uses GitHub Actions Artifacts for efficient index sharing, eliminating reindexing time while keeping the repository lean.

# First time setup - pull latest indexes
python scripts/cli/mcp_cli.py artifact pull --latest

# After making changes - rebuild locally
python scripts/cli/mcp_cli.py index rebuild

# Share your indexes with the team
python scripts/cli/mcp_cli.py artifact push

# Check sync status
python scripts/cli/mcp_cli.py artifact sync

# Optional: Install git hooks for automatic sync
mcp-index hooks install
# Now indexes upload automatically on git push
# and download automatically on git pull

For ANY Repository (MCP Index Kit)

Enable portable index management in any repository with zero GitHub compute costs:

Quick Install

# One-line install
curl -sSL https://raw.githubusercontent.com/yourusername/mcp-index-kit/main/install.sh | bash

# Or via npm
npm install -g mcp-index-kit
mcp-index init

How It Works

1. Zero-Cost Architecture:

   • All indexing happens on developer machines
   • Indexes stored as GitHub Artifacts (free for public repos)
   • Automatic download on clone, upload on push
   • No GitHub Actions compute required

2. Portable Design:

   • Single command setup for any repository
   • Auto-detected by MCP servers and tools
   • Works with all 48 supported languages
   • Enable/disable per repository

3. Usage:

   # Initialize in your repo
   cd your-repo
   mcp-index init
   
   # Build index locally
   mcp-index build
   
   # Push to GitHub Artifacts
   mcp-index push
   
   # Pull latest index
   mcp-index pull
   
   # Auto sync
   mcp-index sync
   

Configuration

Semantic Search Configuration

To enable semantic search capabilities, you need a Voyage AI API key. Get one from https://www.voyageai.com/.

Method 1: Claude Code Configuration (Recommended)

Create or edit .mcp.json in your project root:

{
  "mcpServers": {
    "code-index-mcp": {
      "command": "uvicorn",
      "args": ["mcp_server.gateway:app", "--host", "0.0.0.0", "--port", "8000"],
      "env": {
        "VOYAGE_AI_API_KEY": "your-voyage-ai-api-key-here",
        "SEMANTIC_SEARCH_ENABLED": "true"
      }
    }
  }
}

Method 2: Claude Code CLI

claude mcp add code-index-mcp -e VOYAGE_AI_API_KEY=your_key -e SEMANTIC_SEARCH_ENABLED=true -- uvicorn mcp_server.gateway:app

Method 3: Environment Variables

export VOYAGE_AI_API_KEY=your_key
export SEMANTIC_SEARCH_ENABLED=true

Method 4: .env File

Create a .env file in your project root:

VOYAGE_AI_API_KEY=your_key
SEMANTIC_SEARCH_ENABLED=true

Check Configuration

Verify your semantic search setup:

python scripts/cli/mcp_cli.py index check-semantic

Index Configuration

Edit .mcp-index.json in your repository:

{
  "enabled": true,
  "auto_download": true,
  "artifact_retention_days": 30,
  "github_artifacts": {
    "enabled": true,
    "max_size_mb": 100
  }
}

See for full documentation

View artifact details

python scripts/cli/mcp_cli.py artifact info 12345

#### Index Management
```bash
# Check index status
python scripts/cli/mcp_cli.py index status

# Check compatibility
python scripts/cli/mcp_cli.py index check-compatibility

# Rebuild indexes locally
python scripts/cli/mcp_cli.py index rebuild

# Create backup
python scripts/cli/mcp_cli.py index backup my_backup

# Restore from backup
python scripts/cli/mcp_cli.py index restore my_backup

GitHub Actions Integration

• Pull Requests: Validates developer-provided indexes (no rebuilding)
• Merges to Main: Promotes validated indexes to artifacts
• Cost-Efficient: Uses free GitHub Actions Artifacts storage
• Auto-Cleanup: Old artifacts cleaned up after 30 days

Storage & Cost

• GitHub Actions Artifacts: FREE for public repos, included in private repo quotas
• Retention: 7 days for PR artifacts, 30 days for main branch
• Size Limits: 500MB per artifact (compressed)
• Automatic Compression: ~70% size reduction with tar.gz

Developer Workflow

1. Clone Repository

   git clone https://github.com/yourusername/Code-Index-MCP.git
   cd Code-Index-MCP
   

2. Get Latest Indexes

   python scripts/cli/mcp_cli.py artifact pull --latest
   

3. Make Your Changes

   • Edit code as normal
   • Indexes update automatically via file watcher

4. Share Updates

   # Your indexes are already updated locally
   python scripts/cli/mcp_cli.py artifact push
   

Embedding Model Compatibility

The system tracks embedding model versions to ensure compatibility:

• Current model: voyage-code-3 (1024 dimensions)
• Distance metric: Cosine similarity
• Auto-detection: System checks compatibility before download

If you use a different embedding model, the system will detect incompatibility and rebuild locally with your configuration.

💻 Development

Creating a New Language Plugin

1. Create plugin structure

   mkdir -p mcp_server/plugins/my_language_plugin
   cd mcp_server/plugins/my_language_plugin
   touch __init__.py plugin.py
   

2. Implement the plugin interface

   from mcp_server.plugin_base import PluginBase
   
   class MyLanguagePlugin(PluginBase):
       def __init__(self):
           self.tree_sitter_language = "my_language"
       
       def index(self, file_path: str) -> Dict:
           # Parse and index the file
           pass
       
       def getDefinition(self, symbol: str, context: Dict) -> Dict:
           # Find symbol definition
           pass
       
       def getReferences(self, symbol: str, context: Dict) -> List[Dict]:
           # Find symbol references
           pass
   

3. Register the plugin

   # In dispatcher.py
   from .plugins.my_language_plugin import MyLanguagePlugin
   
   self.plugins['my_language'] = MyLanguagePlugin()
   

Running Tests

# Run all tests
pytest

# Run specific test
pytest test_python_plugin.py

# Run with coverage
pytest --cov=mcp_server --cov-report=html

Architecture Visualization

# View C4 architecture diagrams
docker run --rm -p 8080:8080 \
  -v "$(pwd)/architecture":/usr/local/structurizr \
  structurizr/lite

# Open http://localhost:8080 in your browser

📚 API Reference

Core Endpoints

GET /symbol

Get symbol definition

GET /symbol?symbol_name=parseFile&file_path=/path/to/file.py

Query parameters:

• symbol_name (required): Name of the symbol to find
• file_path (optional): Specific file to search in

GET /search

Search for code patterns

GET /search?query=async+def.*parse&file_extensions=.py,.js

Query parameters:

• query (required): Search pattern (regex supported)
• file_extensions (optional): Comma-separated list of extensions

Response Format

All API responses follow a consistent JSON structure:

Success Response:

{
  "status": "success",
  "data": { ... },
  "timestamp": "2024-01-01T00:00:00Z"
}

Error Response:

{
  "status": "error",
  "error": "Error message",
  "code": "ERROR_CODE",
  "timestamp": "2024-01-01T00:00:00Z"
}

🚢 Deployment

Docker Deployment Options

The project includes multiple Docker configurations for different environments:

Development (Default):

# Uses docker-compose.yml + Dockerfile
docker-compose up -d
# - SQLite database
# - Uvicorn development server  
# - Volume mounts for code changes
# - Debug logging enabled

Production:

# Uses docker-compose.production.yml + Dockerfile.production
docker-compose -f docker-compose.production.yml up -d
# - PostgreSQL database
# - Gunicorn + Uvicorn workers
# - Multi-stage optimized builds
# - Security hardening (non-root user)
# - Production logging

Enhanced Development:

# Uses both compose files with development overrides
docker-compose -f docker-compose.yml -f docker-compose.dev.yml up -d
# - Development base + enhanced debugging
# - Source code volume mounting
# - Read-write code access

Container Restart Behavior

Important: By default, docker-compose restart uses the DEVELOPMENT configuration:

• docker-compose restart → Uses docker-compose.yml (Development)
• docker-compose -f docker-compose.production.yml restart → Uses Production

Production Deployment

For production environments, we provide:

1. Multi-stage Docker builds with security hardening
2. PostgreSQL database with async support
3. Redis caching for performance optimization
4. Qdrant vector database for semantic search
5. Prometheus + Grafana monitoring stack
6. Kubernetes manifests in k8s/ directory
7. nginx reverse proxy configuration

See our for detailed instructions including:

• Kubernetes deployment configurations
• Auto-scaling setup
• Database optimization
• Security best practices
• Monitoring and observability

System Requirements

• Minimum: 2GB RAM, 2 CPU cores, 10GB storage
• Recommended: 8GB RAM, 4 CPU cores, 50GB SSD storage
• Large codebases: 16GB+ RAM, 8+ CPU cores, 100GB+ SSD storage

📦 Releases & Pre-built Indexes

Using Pre-built Indexes

For quick setup, download pre-built indexes from our GitHub releases:

# List available releases
python scripts/download-release.py --list

# Download latest release
python scripts/download-release.py --latest

# Download specific version
python scripts/download-release.py --tag v2024.01.15 --output ./my-index

Creating Releases

Maintainers can create new releases with pre-built indexes:

# Create a new release (as draft)
python scripts/create-release.py --version 1.0.0

# Create and publish immediately
python scripts/create-release.py --version 1.0.0 --publish

Automatic Index Synchronization

The project includes Git hooks for automatic index synchronization:

• Pre-push: Uploads index changes to GitHub artifacts
• Post-merge: Downloads compatible indexes after pulling

Install hooks with: mcp-index hooks install

🤝 Contributing

We welcome contributions! Please see our for details.

Development Process

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests (aim for 90%+ coverage)
5. Update documentation
6. Submit a pull request

Code Style

• Follow PEP 8 for Python code
• Use type hints for all functions
• Write descriptive docstrings
• Keep functions small and focused

📈 Performance

Benchmarks

🏗️ Architecture Overview

The system follows C4 model architecture patterns:

• Workspace Definition: 100% implemented (architecture/workspace.dsl) - Validated with CLI tools
• System Context (L1): Claude Code integration with MCP sub-agent support fully operational
• Container Level (L2): 8 main containers including enhanced MCP server and user documentation
• Component Level (L3): Plugin system with 48 languages, memory management, and cross-repo coordination
• Code Level (L4): 43 PlantUML diagrams documenting all system components and flows

For detailed architectural documentation, see the directory.

🗺️ Development Roadmap

See for detailed development plans and current progress.

Current Status: 100% Complete - Production Ready

• ✅ Completed: All features implemented and validated
• ✅ Documentation: Complete user guides and troubleshooting resources
• ✅ Testing: Comprehensive validation framework with E2E coverage
• ✅ Architecture: All diagrams and documentation aligned with implementation

Recent Achievements (June 2025):

• 🎯 PROJECT COMPLETION: Achieved 100% completion with comprehensive validation
• 🚀 Parallelization Optimization: 81% time reduction in analysis framework (66+ min → 12.5 min)
• 📚 Complete Documentation Suite: Performance tuning, troubleshooting, best practices, and quick start guides
• 🔧 MCP Integration Enhancement: Enhanced sync.py integration with dispatcher optimization
• ✅ Production Validation Framework: Component, integration, E2E, and architecture validation testing
• 🏗️ Architecture Alignment: Updated all Structurizr DSL and PlantUML files to reflect current state
• 📊 Enhanced Performance Analysis: Comprehensive MCP vs Native comparison with token tracking
• 🗂️ Codebase Organization: Cleaned up 200+ analysis files, organized into dedicated directories
• ⚡ Dispatcher Improvements: Fixed timeout issues (5-second protection) and BM25 bypass
• 🔄 Git Synchronization: Automatic index updates tracking repository changes
• 📈 Performance Validation: Sub-100ms query performance across 150K+ files

Optimization Tips

Performance optimization features are implemented and available:

1. Enable caching: Redis caching is implemented and configurable via environment variables
2. Adjust batch size: Configurable via INDEXING_BATCH_SIZE environment variable
3. Use SSD storage: Improves indexing speed significantly
4. Limit file size: Configurable via INDEXING_MAX_FILE_SIZE environment variable
5. Parallel processing: Multi-worker indexing configurable via INDEXING_MAX_WORKERS

🔒 Security

• Local-first: All processing happens locally by default
• Path validation: Prevents directory traversal attacks
• Input sanitization: All queries are sanitized
• Secret detection: Automatic redaction of detected secrets
• Plugin isolation: Plugins run in restricted environments

📄 License

This project is licensed under the MIT License - see the file for details.

🙏 Acknowledgments

• Tree-sitter for language parsing
• Jedi for Python analysis
• FastAPI for the API framework
• Voyage AI for embeddings
• Anthropic for the MCP protocol

📬 Contact

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email:

Built with ❤️ for the developer community