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dbt Semantic Layer MCP Server

dbt Semantic Layer MCP Server

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github.com/TommyBezMemory & Knowledge11Updated 86w ago
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About dbt Semantic Layer MCP Server

dbt Semantic Layer MCP Server connects AI assistants to the dbt Semantic Layer so teams can query defined metrics in natural language. It also supports metric discovery, dimensional analysis, and result display.

Key features

  1. 01Browse and search metrics
  2. 02Create queries using natural language
  3. 03Filter, group, and order data
  4. 04Display query results

Use cases

  1. 01Explore available business metrics
  2. 02Analyze monthly revenue by product category
  3. 03Examine user signup growth trends

MCP support

The MCP server connects to the dbt Semantic Layer and exposes metric discovery, querying, and analysis to compatible AI assistants.

#dbt#semantic-layer#mcp-server

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "dbt-semantic-layer": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli"
      ]
    }
  }
}
Run commandnpx -y @smithery/cli

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@smithery/cli

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

dbt Semantic Layer MCP Server

smithery badge

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

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.