PromptQL MCP Server

Connect Hasura PromptQL to AI assistants like Claude using the Model Context Protocol (MCP).

Overview

This project provides a bridge between Hasura's PromptQL data agent and AI assistants through the Model Context Protocol. With this integration, AI assistants can directly query your enterprise data using natural language, leveraging PromptQL's powerful capabilities for data access, analysis, and visualization.

Features

• 🔍 Natural Language Data Queries - Ask questions about your enterprise data in plain English
• 📊 Table Artifact Support - Get formatted table results from your data queries
• 🔐 Secure Configuration - Safely store and manage your PromptQL API credentials
• 📈 Data Analysis - Get insights and visualizations from your data
• 🛠️ Simple Integration - Works with Claude Desktop and other MCP-compatible clients

Installation

Prerequisites

• Python 3.10 or higher
• A Hasura PromptQL project with API key and DDN URL
• Claude Desktop (for interactive use) or any MCP-compatible client

Install from Source

git clone https://github.com/hasura/promptql-mcp-server.git
cd promptql-mcp-server
pip install -e .

Quick Start

1. Configure your PromptQL credentials:

python -m promptql_mcp_server setup --api-key YOUR_API_KEY --ddn-url YOUR_DDN_URL

2. Test the server:

python -m promptql_mcp_server

3. In a new terminal, try the example client:

python examples/simple_client.py

Using with Claude Desktop

1. Install Claude Desktop
2. Open Claude Desktop and go to Settings > Developer
3. Click "Edit Config" and add the following:

{
  "mcpServers": {
    "promptql": {
      "command": "python",
      "args": ["-m", "promptql_mcp_server"]
    }
  }
}

4. Restart Claude Desktop
5. Chat with Claude and use natural language to query your data

Example Prompts for Claude

• "What were our total sales last quarter?"
• "Who are our top five customers by revenue?"
• "Show me the trend of new user signups over the past 6 months"
• "Which products have the highest profit margin?"

Available Tools and Prompts

Tools

The server exposes the following MCP tools:

• ask_question - Ask natural language questions about your data
• setup_config - Configure PromptQL API key and DDN URL
• check_config - Verify the current configuration status

Prompts

• data_analysis - Create a specialized prompt for data analysis on a specific topic

Architecture

This integration follows a client-server architecture:

1. PromptQL MCP Server - A Python server that exposes PromptQL capabilities through the MCP protocol
2. MCP Client - Any client that implements the MCP protocol (e.g., Claude Desktop)
3. PromptQL API - Hasura's Natural Language API for data access and analysis

The server translates between the MCP protocol and PromptQL's API, allowing seamless integration between AI assistants and your enterprise data.

Development

Project Structure

promptql-mcp-server/
├── promptql_mcp_server/     # Main package
│   ├── __init__.py
│   ├── __main__.py          # Entry point
│   ├── server.py            # MCP server implementation
│   ├── config.py            # Configuration management
│   └── api/                 # API clients
│       ├── __init__.py
│       └── promptql_client.py # PromptQL API client
├── examples/                # Example clients
│   └── simple_client.py     # Simple MCP client
├── setup.py                 # Package configuration
└── README.md                # Documentation

Contributing

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

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Hasura for creating PromptQL
• Anthropic for developing the Model Context Protocol