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KDB MCP Service

by riteshsonawala
GitHub
Python
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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.

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Enables AI agents to interact with KDB+ databases through standardized MCP tools, supporting full CRUD operations, schema introspection, and multi-database connections with connection pooling for efficient time-series and financial data management.

  1. Features
    1. Installation
      1. Configuration
        1. Environment Variables
        2. Configuration File
      2. Usage
        1. Running the Server
        2. Available MCP Tools
      3. Integration with AI Agents
        1. Example Agent Workflow
      4. Project Structure
        1. Security Considerations
          1. Troubleshooting
            1. Connection Issues
            2. Query Errors
          2. Contributing
            1. License
              1. Support

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