index-it-mcp 2.14.7

Local-first code indexer for AI assistants via Model Context Protocol (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

Version: 1.0.0 (MVP Release) Core Features: Stable - Local indexing, symbol/text search, 48-language support Optional Features: Semantic search (requires Voyage AI), Index sync (beta) Performance: Sub-100ms queries, <10s indexing for cached repositories

New to Code-Index-MCP? Check out our Getting Started Guide for a quick walkthrough.

🎯 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 docs/PROJECT_STRUCTURE.md 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:

Category	Languages	Features
Dedicated Plugins	Python, JavaScript, TypeScript, C, C++, Dart, HTML/CSS	Enhanced analysis, framework support
Systems Languages	Go, Rust, C, C++, Zig, Nim, D, V	Memory safety, performance analysis
JVM Languages	Java, Kotlin, Scala, Clojure	Package analysis, build tool integration
Web Technologies	JavaScript, TypeScript, HTML, CSS, SCSS, PHP	Framework detection, bundler support
Scripting Languages	Python, Ruby, Perl, Lua, R, Julia	Dynamic typing, REPL integration
Functional Languages	Haskell, Elixir, Erlang, F#, OCaml	Pattern matching, type inference
Mobile Development	Swift, Kotlin, Dart, Objective-C	Platform-specific APIs
Infrastructure	Dockerfile, Bash, PowerShell, Makefile, CMake	Build automation, CI/CD
Data Formats	JSON, YAML, TOML, XML, GraphQL, SQL	Schema validation, query optimization
Documentation	Markdown, LaTeX, reStructuredText	Cross-references, formatting

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/ViperJuice/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

Feature	Minimal	Standard	Full	Cost
Code Search	✅	✅	✅	Free
48 Languages	✅	✅	✅	Free
Semantic Search	❌	✅	✅	~$0.05/1M tokens
GitHub Sync	❌	✅	✅	Free
Monitoring	❌	❌	✅	Free

🚀 Quickstart (Python)

Prerequisites

• Python 3.12+
• Git

Installation

Option 1: Install via pip (Recommended)

# Install the package
pip install index-it-mcp

# Or install with dev tools for testing
pip install index-it-mcp[dev]

Option 2: Install from Source

# Clone the repository
git clone https://github.com/ViperJuice/Code-Index-MCP.git
cd Code-Index-MCP

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in editable mode
pip install -e .

Quick Start After Installation

# Authenticate GitHub artifact access once
gh auth login

# Pull the latest published index baseline for this repo
mcp-index artifact pull --latest

# Reconcile only your local drift after restore
mcp-index artifact sync

# Check index status
mcp-index index status

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

# Test the API
curl http://localhost:8000/status
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, so most users start from a published index baseline instead of rebuilding locally.

# First time setup - pull latest indexes
mcp-index artifact pull --latest

# After pull, reconcile only your branch/worktree drift
mcp-index artifact sync

# Share your indexes with the team
mcp-index artifact push

# Check sync status
mcp-index 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/ViperJuice/Code-Index-MCP/main/scripts/install-mcp.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 mcp-index-kit for full documentation

View artifact details

mcp-index artifact info 12345

#### Index Management
```bash
# Check index status
mcp-index index status

# Check compatibility
mcp-index index check-compatibility

# Rebuild indexes locally only if artifact sync cannot catch up
mcp-index 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

    gh auth login
    mcp-index artifact pull --latest
   

   • This downloads the current full GitHub artifact snapshot.
   • mcp-index artifact sync then reconciles only your local branch/worktree drift when incremental catch-up is appropriate.

3. Make Your Changes

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

4. Share Updates

   # Your indexes are already updated locally
    mcp-index 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

Multi-profile semantic config can be provided in either:

• SEMANTIC_PROFILES_JSON (environment variable), or
• code-index-mcp.profiles.yaml (repository root).

Artifact Strategy

• GitHub artifact pulls are full snapshot downloads, not partial remote patch fetches.
• The current compressed artifact is modest enough that full downloads stay simpler than a remote delta protocol.
• Efficiency comes from local incremental indexing after restore:
  • pull the latest full artifact
  • compare the restored artifact commit to local HEAD
  • let the watcher or local incremental reindexing reconcile added, modified, deleted, and renamed files
• Branch-specific remote artifacts are optional. The default strategy is to use the latest main artifact as the base and reconcile branch drift locally.

Easy Semantic Setup (Docker-First)

Run onboarding with automatic local Qdrant startup:

python scripts/cli/mcp_cli.py setup semantic

Settings precedence (highest to lowest):

1. CLI flags (for one command run)
2. Environment variables / .env
3. code-index-mcp.profiles.yaml
4. SEMANTIC_PROFILES_JSON
5. Built-in defaults

Common controls:

# Preflight checks only
python scripts/cli/mcp_cli.py setup semantic --dry-run

# Strict mode: fail command if semantic stack isn't ready
python scripts/cli/mcp_cli.py setup semantic --strict

# Override local embedding endpoint
python scripts/cli/mcp_cli.py setup semantic --openai-api-base http://127.0.0.1:8001/v1

Plugin loading is auto-optimized by default using fast repository language detection:

• MCP_AUTO_DETECT_LANGUAGES=true
• MCP_LANGUAGE_DETECT_MAX_FILES=5000
• MCP_LANGUAGE_DETECT_MIN_FILES=2

For startup-sensitive environments, enable:

• MCP_FAST_STARTUP=true (uses lazy plugin loading and skips file watcher startup)

When MCP_AUTO_DETECT_LANGUAGES=true, auto-detection takes precedence over plugins.yaml. Set MCP_AUTO_DETECT_LANGUAGES=false to force plugins.yaml language selection.

For a dual-profile setup (Voyage + local vLLM/Qwen), set:

• VOYAGE_API_KEY (or VOYAGE_AI_API_KEY)
• OPENAI_API_BASE (for example http://127.0.0.1:8000/v1)
• OPENAI_API_KEY (placeholder accepted for local vLLM setups)

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 Deployment Guide 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 Contributing Guide 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

Operation	Performance Target	Current Status
Symbol Lookup	<100ms (p95)	✅ Achieved - All queries < 100ms
Code Search	<500ms (p95)	✅ Achieved - BM25 search < 50ms
File Indexing	10K files/min	✅ Achieved - 152K files indexed

Full-Repo E2E Rerun (2026-03-05)

Latest rerun indexed the full Code-Index-MCP repository using:

• Qwen embeddings via Fireworks (fireworks/qwen3-embedding-8b)
• Voyage embeddings (voyage-code-3)
• Local Qwen embeddings (Qwen/Qwen3-Embedding-8B via tailnet endpoint)
• BM25, fuzzy, classic FTS, and hybrid retrieval

Metric	Result
Files indexed	1282
Semantic-skipped files (oversized)	1
MCP query suite Top-1 pass rate	33.3%
MCP query suite Top-3 pass rate	64.4%
Native tools pass rate (rg/grep/glob)	44.4%
Semantic Qwen latency p50 (single query, top-5)	238.5 ms
Semantic Voyage latency p50 (single query, top-5)	116.68 ms
Semantic Local Qwen latency p50 (single query, top-5)	49.22 ms
BM25 latency p50 (single query, top-5)	46.45 ms
Fuzzy latency p50 (single query, top-5)	10.11 ms
Hybrid semantic-intent latency p50 (single query, top-5)	5093.14 ms

Known gaps from this rerun:

• classic mode still surfaces benchmark/doc artifacts for the semantic-preflight probe.
• hybrid mode still has high tail latency and low Top-1 on intent/persistence probes.
• Top-3 improved materially, but Top-1 remains a ranking problem.

Benchmark artifacts:

• docs/benchmarks/e2e_retrieval_validation_fullrepo_fireworks_qwen_voyage_local_iter5_rerun.json
• docs/benchmarks/mcp_vs_native_benchmark_fullrepo_fireworks_qwen_voyage_local_iter5_rerun.json
• docs/benchmarks/fullrepo-benchmark-analysis.md

🏗️ 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 architecture/ directory.

🗺️ Development Roadmap

See ROADMAP.md for detailed development plans and current progress.

Current Status: v1.0.0 MVP Release

• ✅ Core Indexing: SQLite + FTS5 for fast local search
• ✅ Multi-Language: 48 languages via tree-sitter integration
• ✅ MCP Protocol: Full compatibility with Claude Code and other MCP clients
• ✅ Performance: Sub-100ms queries with BM25 optimization
• 🔄 Index Sync: Beta support via GitHub Artifacts
• 🔄 Semantic Search: Optional feature requiring Voyage AI API

Recent Improvements:

• ⚡ Dispatcher Optimization: Timeout protection and BM25 bypass for reliability
• 🔄 Hybrid Search: BM25 + semantic search with graceful degradation
• 📊 Result Ranking: Improved relevance with score normalization
• 🔧 CLI Tools: Full-featured mcp-index command for index management

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 LICENSE 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

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