📌Overview

Purpose: The dbt Semantic Layer MCP Server facilitates seamless querying of business metrics through AI assistants, enhancing data accessibility and analysis.

Overview: This Model-Connector-Presenter (MCP) server serves as a bridge between AI tools like Claude and the dbt Semantic Layer. It allows users to query metrics using natural language, making complex data analysis more approachable for all team members.

Key Features:

Metric Discovery: Enables users to browse and search for available metrics in the dbt Semantic Layer, ensuring ease of access.
Query Creation: Allows users to generate and execute semantic queries through natural language, simplifying interactions with data.
Data Analysis: Provides functionalities to filter, group, and order metrics, empowering users to gain deeper insights into their data.
Result Visualization: Displays query results in a user-friendly format, making the insights easily understandable and actionable.

dbt Semantic Layer MCP Server

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:

Ask about available metrics: "What metrics are available in my dbt Semantic Layer?"
Query specific metrics: "Show me monthly revenue for the last quarter grouped by product category"
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

dbt-semantic-layer-mcp-server