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TommyBez

dbt Semantic Layer MCP Server

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

Related MCP server: MCP Iceberg Catalog

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

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

No tool schema history has been recorded yet.

Related MCP Connectors

  • Ask data questions in natural language. Get SQL, insights, and charts from your databases.

  • The Ramp MCP server enables users to securely connect Ramp with AI assistants like ChatGPT and Claude to query financial data and take actions using natural language. It transforms Ramp's developer API into a SQL interface that LLMs can query, allowing admins to analyze spend trends, identify cost savings, and run complex SQL analyses on comprehensive datasets (transactions, purchase orders, vendors, users), while all users can manage cards, view transactions, request reimbursements, and get expense policy answers.

  • The grounded data layer for any LLM: governed SQL, metrics, lineage and catalog over your data.

  • Query your warehouse or a CSV with Claude/ChatGPT over MCP, governed by table-level ACL + audit.

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