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

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.

• 🔍 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

• 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

1. Clone the repository:

git clone https://github.com/hasura/promptql-mcp.git
cd promptql-mcp

2. Set up a virtual environment (recommended):

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the package:

pip install -e .

1. Configure your PromptQL credentials:

python -m promptql_mcp_server setup --api-key YOUR_PROMPTQL_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

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

{
  "mcpServers": {
    "promptql": {
      "command": "/full/path/to/python",
      "args": ["-m", "promptql_mcp_server"]
    }
  }
}

Replace /full/path/to/python with the actual path to your Python executable.

If you're using a virtual environment (recommended):

{
  "mcpServers": {
    "promptql": {
      "command": "/path/to/your/project/venv/bin/python",
      "args": ["-m", "promptql_mcp_server"]
    }
  }
}

To find your Python path, run:

which python  # On macOS/Linux
where python  # On Windows

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

• "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?"

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

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

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.

On many systems, especially macOS, you may need to use python3 and pip3 instead of python and pip.

Modern Python installations often prevent global package installation. Use a virtual environment as described in the installation section.

Ensure you've:

1. Installed the package with pip install -e .
2. Are using the correct Python environment (if using a virtual environment, make sure it's activated)
3. Configured Claude Desktop to use the correct Python executable path

If you have multiple Python versions installed, make sure you're using Python 3.10 or higher:

python3.10 -m venv venv  # Specify the exact version

promptql-mcp/
├── 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

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

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

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