dbt Semantic Layer MCP Server

dbt Semantic Layer MCP Server
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MCP Server for querying DBT Semantic Layer. Contribute to TommyBez/dbt-semantic-layer-mcp-server development by creating an account on GitHub.

TommyBez

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A Model-Connector-Presenter (MCP) server for seamlessly querying the dbt Semantic Layer through Claude Desktop and other compatible AI assistants.

What is the dbt Semantic Layer?

The dbt Semantic Layer is a powerful feature that allows you to define metrics once in your dbt project and reuse them consistently across your entire data stack. It provides:

  • A single source of truth for business metrics
  • Consistent metric definitions across all data tools
  • Simplified access to complex metrics for all team members

About This Project

This MCP server acts as a bridge between AI assistants (like Claude) and the dbt Semantic Layer, enabling you to:

  • Query metrics directly through natural language conversations
  • Explore available metrics and their definitions
  • Analyze data with dimensional breakdowns and filters
  • Visualize results within your AI assistant interface

Features

  • 🔍 Metric Discovery: Browse and search available metrics in your dbt Semantic Layer
  • 📊 Query Creation: Generate and execute semantic queries through natural language
  • 🧮 Data Analysis: Filter, group, and order metrics for deeper insights
  • 📈 Result Visualization: Display query results in an easy-to-understand format

Prerequisites

  • A dbt Cloud account with Semantic Layer enabled
  • API access to your dbt Cloud instance
  • Node.js (v14 or later)

Installation

Via Smithery (Recommended)

The easiest way to install is via Smithery:

npx -y @smithery/cli install @TommyBez/dbt-semantic-layer-mcp --client claude

Usage

Once installed and configured, you can interact with the dbt Semantic Layer directly from Claude Desktop:

  1. Ask about available metrics: "What metrics are available in my dbt Semantic Layer?"
  2. Query specific metrics: "Show me monthly revenue for the last quarter grouped by product category"
  3. Analyze trends: "What's the week-over-week growth in user signups?"

Troubleshooting

If you encounter issues:

  • Verify your API credentials are correct
  • Ensure your dbt Cloud project has Semantic Layer enabled
  • Check that your metrics are properly defined in your dbt project

Contributing

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

License

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

Acknowledgments

  • dbt Labs for creating the dbt Semantic Layer
  • Smithery for the MCP deployment platform
  • LiteMCP for the MCP development package

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