KDB MCP Service

A Model Context Protocol (MCP) service for interacting with KDB+ databases. This service allows AI agents to query, insert, update, and delete data from KDB+ databases through a standardized MCP interface.

Features

• Multiple Database Support: Connect to multiple KDB+ databases simultaneously
• Connection Pooling: Efficient connection management with configurable pool sizes
• Full CRUD Operations: Query, insert, update, and delete data
• Schema Introspection: List tables and get schema information
• Environment Variable Support: Secure credential management via environment variables
• Async Operations: Non-blocking database operations for better performance

Installation

1. Clone the repository:

git clone <repository-url>
cd kdb-mcp

2. Install dependencies:

pip install -r requirements.txt

3. Configure your databases (see Configuration section)

Configuration

Environment Variables

Copy .env.example to .env and fill in your database credentials:

cp .env.example .env

Edit .env with your actual database details:

KDB_PROD_HOST=your-prod-host.com
KDB_PROD_PORT=5000
KDB_PROD_USERNAME=your-username
KDB_PROD_PASSWORD=your-password

Configuration File

The service uses a YAML configuration file located at config/kdb_config.yaml. You can customize:

• Database connections
• Connection pool sizes
• Logging settings
• Server configuration

Example configuration:

databases:
  production:
    host: ${KDB_PROD_HOST:localhost}
    port: ${KDB_PROD_PORT:5000}
    username: ${KDB_PROD_USERNAME:}
    password: ${KDB_PROD_PASSWORD:}
    pool_size: 10
    description: Production KDB+ database

Usage

Running the Server

Start the MCP server:

python main.py

Or with a custom config file:

python main.py /path/to/custom/config.yaml

Available MCP Tools

The service provides the following MCP tools:

1. kdb_query

Execute any Q query on a KDB+ database.

{
  "database": "production",
  "query": "select from trades where date=.z.d"
}

2. kdb_list_tables

List all tables in a database.

{
  "database": "production"
}

3. kdb_get_schema

Get schema information for a specific table.

{
  "database": "production",
  "table": "trades"
}

4. kdb_select

Execute a SELECT query with optional filtering.

{
  "database": "production",
  "table": "trades",
  "columns": ["symbol", "price", "volume"],
  "where": "symbol=`AAPL",
  "limit": 100
}

5. kdb_insert

Insert data into a table.

{
  "database": "production",
  "table": "trades",
  "data": {
    "symbol": "AAPL",
    "price": 150.25,
    "volume": 1000
  }
}

6. kdb_update

Update existing records in a table.

{
  "database": "production",
  "table": "trades",
  "updates": {
    "price": 151.00
  },
  "where": "symbol=`AAPL"
}

7. kdb_delete

Delete records from a table.

{
  "database": "production",
  "table": "trades",
  "where": "date<.z.d-30"
}

8. kdb_list_databases

List all configured databases.

{}

Integration with AI Agents

This MCP service can be integrated with any AI agent that supports the Model Context Protocol. The agent can use the provided tools to:

1. Query real-time market data
2. Analyze historical trading patterns
3. Update trading strategies
4. Manage data pipelines
5. Generate reports from KDB+ data

Example Agent Workflow

# Agent pseudocode
async def analyze_trading_data():
    # List available databases
    databases = await call_tool("kdb_list_databases", {})

    # Get today's trades
    trades = await call_tool("kdb_select", {
        "database": "production",
        "table": "trades",
        "where": "date=.z.d",
        "limit": 1000
    })

    # Analyze and generate insights
    insights = analyze(trades)

    # Store insights back to KDB+
    await call_tool("kdb_insert", {
        "database": "analytics",
        "table": "insights",
        "data": insights
    })

Project Structure

kdb-mcp/
├── src/
│   └── kdb_mcp/
│       ├── __init__.py           # Package initialization
│       ├── kdb_connection.py     # KDB+ connection handling
│       ├── mcp_server.py         # MCP server implementation
│       └── config.py             # Configuration management
├── config/
│   └── kdb_config.yaml          # Database configuration
├── main.py                       # Entry point
├── requirements.txt              # Python dependencies
├── .env.example                  # Environment variables template
└── README.md                     # This file

Security Considerations

• Never commit .env files with actual credentials
• Use environment variables for sensitive information
• Implement proper authentication for production deployments
• Consider using SSL/TLS for database connections
• Regularly rotate database credentials
• Limit database permissions to minimum required

Troubleshooting

Connection Issues

• Verify KDB+ server is running and accessible
• Check firewall rules for the KDB+ port
• Ensure credentials are correct
• Test connectivity with telnet host port

Query Errors

• Verify Q syntax is correct
• Check table and column names exist
• Ensure proper data types are used
• Review KDB+ server logs for detailed errors

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

[Your License Here]

Support

For issues and questions, please create an issue in the repository or contact your system administrator.