Which integrations are available for this server?

Offers instructions for running Qdrant vector database via Docker containers to support the semantic search functionality Provides a Node.js library interface for programmatic integration, with specific adapters and APIs for Node.js environments Leverages Ollama for embedding models to power semantic code search, with specific support for models like nomic-embed-text

How do I use AutoDev Codebase MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AutoDev Codebase MCP Server search for user authentication functions in the current project" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

@autodev/codebase

npm version GitHub stars License: MIT

╭─ ~/workspace/autodev-codebase ╰─❯ codebase --demo --search="user manage" Found 3 results in 2 files for: "user manage" ================================================== File: "hello.js" ================================================== < class UserManager > (L7-20) class UserManager { constructor() { this.users = []; } addUser(user) { this.users.push(user); console.log('User added:', user.name); } getUsers() { return this.users; } } ================================================== File: "README.md" | 2 snippets ================================================== < md_h1 Demo Project > md_h2 Usage > md_h3 JavaScript Functions > (L16-20) ### JavaScript Functions - greetUser(name) - Greets a user by name - UserManager - Class for managing user data ───── < md_h1 Demo Project > md_h2 Search Examples > (L27-38) ## Search Examples Try searching for: - "greet user" - "process data" - "user management" - "batch processing" - "YOLO model" - "computer vision" - "object detection" - "model training"

A vector embedding-based code semantic search tool with MCP server and multi-model integration. Can be used as a pure CLI tool. Supports Ollama for fully local embedding and reranking, enabling complete offline operation and privacy protection for your code repository.

🚀 Features

🔍 Semantic Code Search: Vector-based search using advanced embedding models
🌐 MCP Server: HTTP-based MCP server with SSE and stdio adapters
💻 Pure CLI Tool: Standalone command-line interface without GUI dependencies
⚙️ Layered Configuration: CLI, project, and global config management
🎯 Advanced Path Filtering: Glob patterns with brace expansion and exclusions
🌲 Tree-sitter Parsing: Support for 40+ programming languages
💾 Qdrant Integration: High-performance vector database
🔄 Multiple Providers: OpenAI, Ollama, Jina, Gemini, Mistral, OpenRouter, Vercel
📊 Real-time Watching: Automatic index updates
⚡ Batch Processing: Efficient parallel processing
📝 Code Outline Extraction: Generate structured code outlines with AI summaries

Related MCP server: code-index-mcp

📦 Installation

1. Dependencies

brew install ollama ripgrep ollama serve ollama pull nomic-embed-text

2. Qdrant

docker run -d -p 6333:6333 -p 6334:6334 --name qdrant qdrant/qdrant

3. Install

npm install -g @autodev/codebase codebase --set-config embedderProvider=ollama,embedderModelId=nomic-embed-text

🛠️ Quick Start

# Demo mode (recommended for first-time) # Creates a demo directory in current working directory for testing # Index & search codebase --demo --index codebase --demo --search="user greet" # MCP server codebase --demo --serve

📋 Commands

📝 AI-Powered Code Outlines

Generate intelligent code summaries with one command:

codebase --outline "src/**/*.ts" --summarize

Output Example:

# src/cli.ts (1902 lines) └─ Implements a simplified CLI for @autodev/codebase using Node.js native parseArgs. Manages codebase indexing, searching, and MCP server operations. 27--35 | function initGlobalLogger └─ Initializes a global logger instance with specified log level and timestamps. 45--54 | interface SearchResult └─ Defines the structure for search result payloads, including file path, code chunk, and relevance score. ... (full outline with AI summaries)

Benefits:

🧠 Understand code fast - Get function-level summaries without reading every line
💾 Smart caching - Only summarizes changed code blocks
🌐 Multi-language - English/Chinese summaries supported
⚡ Batch processing - Efficiently handles large codebases

Quick Setup:

# Configure Ollama (recommended for free, local AI) codebase --set-config summarizerProvider=ollama,summarizerOllamaModelId=qwen3-vl:4b-instruct # Or use DeepSeek (cost-effective API) codebase --set-config summarizerProvider=openai-compatible,summarizerOpenAiCompatibleBaseUrl=https://api.deepseek.com/v1,summarizerOpenAiCompatibleModelId=deepseek-chat,summarizerOpenAiCompatibleApiKey=sk-your-key

Indexing & Search

# Index the codebase codebase --index --path=/my/project --force # Search with filters codebase --search="error handling" --path-filters="src/**/*.ts" # Search with custom limit and minimum score codebase --search="authentication" --limit=20 --min-score=0.7 codebase --search="API" -l 30 -S 0.5 # Search in JSON format codebase --search="authentication" --json # Clear index data codebase --clear --path=/my/project

MCP Server

# HTTP mode (recommended) codebase --serve --port=3001 --path=/my/project # Stdio adapter codebase --stdio-adapter --server-url=http://localhost:3001/mcp

Configuration

# View config codebase --get-config codebase --get-config embedderProvider --json # Set config codebase --set-config embedderProvider=ollama,embedderModelId=nomic-embed-text codebase --set-config --global qdrantUrl=http://localhost:6333

Advanced Features

🔍 LLM-Powered Search Reranking

Enable LLM reranking to dramatically improve search relevance:

# Enable reranking with Ollama (recommended) codebase --set-config rerankerEnabled=true,rerankerProvider=ollama,rerankerOllamaModelId=qwen3-vl:4b-instruct # Or use OpenAI-compatible providers codebase --set-config rerankerEnabled=true,rerankerProvider=openai-compatible,rerankerOpenAiCompatibleModelId=deepseek-chat # Search with automatic reranking codebase --search="user authentication" # Results are automatically reranked by LLM

Benefits:

🎯 Higher precision: LLM understands semantic relevance beyond vector similarity
📊 Smart scoring: Results are reranked on a 0-10 scale based on query relevance
⚡ Batch processing: Efficiently handles large result sets with configurable batch sizes
🎛️ Threshold control: Filter results with rerankerMinScore to keep only high-quality matches

Path Filtering & Export

# Path filtering with brace expansion and exclusions codebase --search="API" --path-filters="src/**/*.ts,lib/**/*.js" codebase --search="utils" --path-filters="{src,test}/**/*.ts" # Export results in JSON format for scripts codebase --search="auth" --json

📝 AI-Powered Code Outlines

Generate intelligent code summaries and outlines:

# Extract code structure codebase --outline src/index.ts # With AI summaries (recommended) codebase --outline "src/**/*.ts" --summarize # Preview before processing codebase --outline "src/**/*.ts" --dry-run # Clear cache and regenerate codebase --outline src/index.ts --summarize --clear-summarize-cache

Key Benefits:

🎯 Function-level summaries: Understand code purpose at a glance
💾 Smart caching: Avoid redundant LLM calls
🌐 Multi-language: English / Chinese support
⚡ Batch processing: Efficiently handle large codebases

⚙️ Configuration

Config Layers (Priority Order)

CLI Arguments - Runtime parameters (--path, --config, --log-level, --force, etc.)
Project Config - ./autodev-config.json (or custom path via --config)
Global Config - ~/.autodev-cache/autodev-config.json
Built-in Defaults - Fallback values

Note: CLI arguments provide runtime override for paths, logging, and operational behavior. For persistent configuration (embedderProvider, API keys, search parameters), use --set-config to save to config files.

Common Config Examples

Ollama:

{ "embedderProvider": "ollama", "embedderModelId": "nomic-embed-text", "qdrantUrl": "http://localhost:6333" }

OpenAI:

{ "embedderProvider": "openai", "embedderModelId": "text-embedding-3-small", "embedderOpenAiApiKey": "sk-your-key", "qdrantUrl": "http://localhost:6333" }

OpenAI-Compatible:

{ "embedderProvider": "openai-compatible", "embedderModelId": "text-embedding-3-small", "embedderOpenAiCompatibleApiKey": "sk-your-key", "embedderOpenAiCompatibleBaseUrl": "https://api.openai.com/v1" }

Key Configuration Options

Category	Options	Description
Embedding	`embedderProvider`, `embedderModelId`, `embedderModelDimension`	Provider and model settings
API Keys	`embedderOpenAiApiKey`, `embedderOpenAiCompatibleApiKey`	Authentication
Vector Store	`qdrantUrl`, `qdrantApiKey`	Qdrant connection
Search	`vectorSearchMinScore`, `vectorSearchMaxResults`	Search behavior
Reranker	`rerankerEnabled`, `rerankerProvider`	Result reranking
Summarizer	`summarizerProvider`, `summarizerLanguage`, `summarizerBatchSize`	AI summary generation

Key CLI Arguments:

--serve / --index / --search - Core operations
--outline <pattern> - Extract code outlines (supports glob patterns)
--summarize - Generate AI summaries for code outlines
--dry-run - Preview operations before execution
--title - Show only file-level summaries
--clear-summarize-cache - Clear all summary caches
--get-config / --set-config - Configuration management
--path, --demo, --force - Common options
--limit / -l <number> - Maximum number of search results (default: from config, max 50)
--min-score / -S <number> - Minimum similarity score for search results (0-1, default: from config)
--help - Show all available options

For complete CLI reference, see CONFIG.md.

Configuration Commands:

# View config codebase --get-config codebase --get-config --json # Set config (saves to file) codebase --set-config embedderProvider=ollama,embedderModelId=nomic-embed-text codebase --set-config --global embedderProvider=openai,embedderOpenAiApiKey=sk-xxx # Use custom config file codebase --config=/path/to/config.json --get-config codebase --config=/path/to/config.json --set-config embedderProvider=ollama # Runtime override (paths, logging, etc.) codebase --index --path=/my/project --log-level=info --force

For complete configuration reference, see CONFIG.md.

🔌 MCP Integration

HTTP Streamable Mode (Recommended)

codebase --serve --port=3001

IDE Config:

{ "mcpServers": { "codebase": { "url": "http://localhost:3001/mcp" } } }

Stdio Adapter

# First start the MCP server in one terminal codebase --serve --port=3001 # Then connect via stdio adapter in another terminal (for IDEs that require stdio) codebase --stdio-adapter --server-url=http://localhost:3001/mcp

IDE Config:

{ "mcpServers": { "codebase": { "command": "codebase", "args": ["stdio-adapter", "--server-url=http://localhost:3001/mcp"] } } }

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue on GitHub.

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

This project is a fork and derivative work based on Roo Code. We've built upon their excellent foundation to create this specialized codebase analysis tool with enhanced features and MCP server capabilities.

🌟 If you find this tool helpful, please give us a

Made with ❤️ for the developer community

AutoDev Codebase MCP Server

@autodev/codebase

🚀 Features

📦 Installation

1. Dependencies

2. Qdrant

3. Install

🛠️ Quick Start

📋 Commands

📝 AI-Powered Code Outlines

Indexing & Search

MCP Server

Configuration

Advanced Features

🔍 LLM-Powered Search Reranking

Path Filtering & Export

📝 AI-Powered Code Outlines

⚙️ Configuration

Config Layers (Priority Order)

Common Config Examples

Key Configuration Options

🔌 MCP Integration

HTTP Streamable Mode (Recommended)

Stdio Adapter

🤝 Contributing

📄 License

🙏 Acknowledgments

Resources

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