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KDB MCP Service

by riteshsonawala
OverviewInspectNewSchemaRelated ServersScore
Python
Remote

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

  1. Install dependencies:

pip install -r requirements.txt

  1. 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.

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