dbt-mcp
The dbt-mcp provides a Model Context Protocol server that facilitates interaction with dbt models, including executing, profiling, and managing models, metadata retrieval, and integration with tools like IDEs and data platforms.
Added on:
Created by:
Apr 27 2025
The dbt-mcp provides a Model Context Protocol server that facilitates interaction with dbt models, including executing, profiling, and managing models, metadata retrieval, and integration with tools like IDEs and data platforms.
Added on:
Created by:
Apr 27 2025
dbt Labs
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What is dbt-mcp?
This MCP (Model Context Protocol) server for dbt enables advanced management and interaction with dbt data models. It offers features such as executing models, generating documentation, retrieving model details, and integrating with various data tools. Its architecture supports automation and programmatic control over dbt projects, making it ideal for data engineers, analysts, and Plattform integrations seeking streamlined model management and data pipeline orchestration. Users can deploy and connect to the server via CLI, IDE, or cloud configurations, facilitating scalable and collaborative data operations.
Who will use dbt-mcp?
- Data Engineers
- Data Analysts
- Platform Integrators
- Data Engineering Teams
- Data Platform Developers
How to use the dbt-mcp?
- Step1: Install the MCP server using the provided install script.
- Step2: Configure the server by setting environment variables for dbt host, token, and environment ID.
- Step3: Connect your MCP client (e.g., VS Code, Cursor, Claude Desktop) to the server using the configuration instructions.
- Step4: Use the MCP client or API to run dbt commands, retrieve model details, or manage your dbt models.
- Step5: Monitor and manage the server via command palette or configuration files to start, stop, or adjust the server as needed.
dbt-mcp's Core Features & Benefits
The Core Features
- Execute dbt models and commands
- Retrieve model metadata and details
- Generate dbt documentation
- Query and list dbt models and metrics
- Integrate with IDEs and cloud platforms
The Benefits
- Streamlines management of dbt models
- Enables programmatic control over data pipelines
- Supports automation and integration with various tools
- Facilitates collaboration across data teams
- Provides a scalable solution for model metadata management
dbt-mcp's Main Use Cases & Applications
- Automated execution and testing of dbt models in CI/CD pipelines
- Metadata management and documentation generation for data models
- Integration with IDEs for real-time model interaction and debugging
- Data pipeline orchestration and monitoring
- Platform integration for managing multiple dbt environments
FAQs of dbt-mcp
Developer
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