Enterprise Code Search MCP Server

A powerful Model Context Protocol (MCP) server for semantic code search with shared vector database. Supports both OpenAI and Ollama for embeddings, and can index local projects or Git repositories.

🚀 Features

• Semantic code search using AI embeddings

• Dual provider support: OpenAI or Ollama (local, private)

• Flexible indexing: Local projects or Git repositories

• Shared vector database with ChromaDB

• Multi-project management: Handle multiple projects simultaneously

• Automatic project structure analysis

• Similar code search based on code snippets

• Enterprise-ready: Private, secure, self-hosted

📋 Requirements

• Node.js 18+

• Docker and Docker Compose

• Git (for repository indexing)

🛠️ Quick Start

1. Clone the repository

2. Install dependencies

3. Configure environment

4. Start services

5. Build and run

⚙️ Configuration

Using Ollama (Recommended for Enterprise)

Using OpenAI

🔧 Claude Desktop Integration

To use this MCP server with Claude Desktop, add to your claude_desktop_config.json:

🎯 Usage Examples

1. Index a local project

2. Search in code

3. Find similar code

4. Analyze project structure

🛠️ Available Tools

📊 Example Queries

Functional searches

• "Where is the authentication logic?"

• "Functions that handle database operations"

• "Environment variable configuration"

• "Unit tests for the API"

Code analysis

• "What design patterns are used?"

• "Most complex functions in the project"

• "Error handling in the code"

Technology-specific search

• "Code using React hooks"

• "PostgreSQL queries"

• "Docker configuration"

🔧 Advanced Configuration

Recommended Ollama Models

File Patterns

The server supports extensive file type recognition including:

• Programming Languages: Python, JavaScript/TypeScript, Java, C/C++, Go, Rust, PHP, Ruby, Swift, Kotlin, Scala, and more

• Web Technologies: HTML, CSS, SCSS, Vue, Svelte

• Configuration: JSON, YAML, TOML, Docker, Terraform

• Documentation: Markdown, reStructuredText, AsciiDoc

• Database: SQL files

Performance Tuning

🏢 Enterprise Deployment

Option 1: Dedicated Server

Option 2: Network Deployment

🔒 Security Considerations

Key Benefits

1. Private Data: Ollama keeps everything local

2. No External APIs: When using Ollama, no data leaves your network

3. Self-hosted: Full control over your code and embeddings

4. Isolated Environment: Docker containers provide isolation

Security Best Practices

📈 Monitoring & Troubleshooting

Useful Logs

Common Issues

Ollama not responding:

ChromaDB slow:

Poor embedding quality:

• Try different model: all-minilm vs nomic-embed-text

• Adjust chunk size

• Verify source file quality

🤝 Collaborative Workflow

Typical Enterprise Workflow

1. DevOps indexes main projects

2. Developers search code using Claude

3. Automatic updates via CI/CD

4. Code analysis for code reviews

Best Practices

• Index after important merges

• Use descriptive project names

• Maintain project-specific search filters

• Document naming conventions

🛠️ Development

Project Structure

Available Scripts

📚 API Reference

The MCP server implements the standard Model Context Protocol with these specific tools:

• index_local_project: Index local directories with configurable file patterns

• search_codebase: Semantic search with project filtering and similarity scoring

• list_indexed_projects: Enumerate all indexed projects with metadata

• get_embedding_provider_info: Get current provider status and configuration

Each tool includes detailed JSON schema with examples and validation.

🤖 Recommended AI Models

For embeddings (Ollama)

• nomic-embed-text: Optimized for code

• all-minilm: Balanced, fast

• mxbai-embed-large: High precision

For embeddings (OpenAI)

• text-embedding-3-small: Cost-effective

• text-embedding-3-large: Higher precision

🐳 Docker Support

The project includes a complete Docker setup:

• ChromaDB: Vector database for embeddings

• Ollama: Local embedding generation

• PostgreSQL: Optional metadata storage

All services are orchestrated with Docker Compose for easy deployment.

☕ Support

If this project helps you with your development workflow, consider supporting it:

📄 License

MIT License - see LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the project

2. Create your feature branch (git checkout -b feature/AmazingFeature)

3. Commit your changes (git commit -m 'Add some AmazingFeature')

4. Push to the branch (git push origin feature/AmazingFeature)

5. Open a Pull Request

📞 Support & Issues

• 📧 Issues: GitHub Issues

• 💬 Discussions: GitHub Discussions

• ☕ Support: Buy Me a Coffee