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

Private Data: Ollama keeps everything local
No External APIs: When using Ollama, no data leaves your network
Self-hosted: Full control over your code and embeddings
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

DevOps indexes main projects
Developers search code using Claude
Automatic updates via CI/CD
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:

📄 License

MIT License - see LICENSE file for details.

🤝 Contributing

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

Fork the project
Create your feature branch (git checkout -b feature/AmazingFeature)
Commit your changes (git commit -m 'Add some AmazingFeature')
Push to the branch (git push origin feature/AmazingFeature)
Open a Pull Request

📞 Support & Issues

📧 Issues: GitHub Issues
💬 Discussions: GitHub Discussions
☕ Support: Buy Me a Coffee