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Enterprise Code Search MCP Server

by damian-pramparo
GitHub
TypeScript
MIT License
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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

git clone https://github.com/your-username/semantic-context-mcp.git cd semantic-context-mcp

2. Install dependencies

npm install

3. Configure environment

cp .env.example .env # Edit .env with your configuration

4. Start services

# Start ChromaDB and Ollama docker-compose up -d # Wait for Ollama to download models docker-compose logs -f ollama-setup

5. Build and run

npm run build npm start

⚙️ Configuration

Using Ollama (Recommended for Enterprise)

# .env EMBEDDING_PROVIDER=ollama OLLAMA_HOST=http://localhost:11434 OLLAMA_MODEL=nomic-embed-text CHROMA_HOST=localhost CHROMA_PORT=8000

Using OpenAI

# .env EMBEDDING_PROVIDER=openai OPENAI_API_KEY=your-api-key OPENAI_MODEL=text-embedding-3-small

🔧 Claude Desktop Integration

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

{ "mcpServers": { "enterprise-code-search": { "command": "node", "args": ["/path/to/semantic-context-mcp/dist/index.js"], "env": { "EMBEDDING_PROVIDER": "ollama", "OLLAMA_HOST": "http://localhost:11434", "OLLAMA_MODEL": "nomic-embed-text", "CHROMA_HOST": "localhost", "CHROMA_PORT": "8000", "COMPANY_NAME": "YourCompany" } } } }

🎯 Usage Examples

1. Index a local project

Index my local project at /home/user/my-app with the name "frontend-app"

2. Search in code

Search for "main application function" in all indexed projects

3. Find similar code

Find code similar to: ```python def authenticate_user(username, password): return check_credentials(username, password)

4. Analyze project structure

Analyze the structure of project "frontend-app"

🛠️ Available Tools

Tool

Description

index_local_project

Index a local directory

search_codebase

Semantic search in code

list_indexed_projects

List all indexed projects

get_embedding_provider_info

Get embedding provider information

📊 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

# For code embeddings ollama pull nomic-embed-text # Best for code (384 dims) ollama pull all-minilm # Lightweight alternative (384 dims) ollama pull mxbai-embed-large # Higher precision (1024 dims)

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

# Maximum chunk size (characters) MAX_CHUNK_SIZE=1500 # Maximum file size (KB) MAX_FILE_SIZE=500 # Batch size for indexing BATCH_SIZE=100

🏢 Enterprise Deployment

Option 1: Dedicated Server

# On enterprise server docker-compose up -d

Option 2: Network Deployment

# Configure for network access CHROMA_HOST=192.168.1.100 OLLAMA_HOST=http://192.168.1.100:11434

🔒 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

# Restrict ChromaDB access CHROMA_SERVER_HOST=127.0.0.1 # Localhost only # Use HTTPS for production OLLAMA_HOST=https://ollama.company.com

📈 Monitoring & Troubleshooting

Useful Logs

# View indexing logs docker-compose logs -f enterprise-mcp-server # ChromaDB performance docker-compose logs -f chromadb # Monitor Ollama curl http://localhost:11434/api/tags

Common Issues

Ollama not responding:

curl http://localhost:11434/api/tags # If it fails: docker-compose restart ollama

ChromaDB slow:

# Check disk space docker system df # Clean if necessary docker system prune

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

src/ ├── index.ts # Main MCP server └── http-server.ts # HTTP server variant scripts/ # Setup and utility scripts docker-compose.yml # Service orchestration package.json # Dependencies and scripts

Available Scripts

npm run build # Compile TypeScript npm run dev # Development mode npm run start # Production mode npm run clean # Clean build directory

📚 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:

Buy Me A Coffee

📄 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

Deploy Server
-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

Enables semantic code search across local projects and Git repositories using AI embeddings with ChromaDB. Supports both OpenAI and local Ollama models for private, enterprise-ready code analysis and similar code discovery.

  1. 🚀 Features
    1. 📋 Requirements
      1. 🛠️ Quick Start
        1. 1. Clone the repository
        2. 2. Install dependencies
        3. 3. Configure environment
        4. 4. Start services
        5. 5. Build and run
      2. ⚙️ Configuration
        1. Using Ollama (Recommended for Enterprise)
        2. Using OpenAI
      3. 🔧 Claude Desktop Integration
        1. 🎯 Usage Examples
          1. 1. Index a local project
          2. 2. Search in code
          3. 3. Find similar code
          4. 4. Analyze project structure
        2. 🛠️ Available Tools
          1. 📊 Example Queries
            1. Functional searches
            2. Code analysis
            3. Technology-specific search
          2. 🔧 Advanced Configuration
            1. Recommended Ollama Models
            2. File Patterns
            3. Performance Tuning
          3. 🏢 Enterprise Deployment
            1. Option 1: Dedicated Server
            2. Option 2: Network Deployment
          4. 🔒 Security Considerations
            1. Key Benefits
            2. Security Best Practices
          5. 📈 Monitoring & Troubleshooting
            1. Useful Logs
            2. Common Issues
          6. 🤝 Collaborative Workflow
            1. Typical Enterprise Workflow
            2. Best Practices
          7. 🛠️ Development
            1. Project Structure
            2. Available Scripts
          8. 📚 API Reference
            1. 🤖 Recommended AI Models
              1. For embeddings (Ollama)
              2. For embeddings (OpenAI)
            2. 🐳 Docker Support
              1. ☕ Support
                1. 📄 License
                  1. 🤝 Contributing
                    1. 📞 Support & Issues

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