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dbt CLI MCP Server

# dbt CLI MCP Server ## Overview The dbt CLI MCP Server provides tools for running dbt commands through the Model Context Protocol. It allows AI assistants to execute dbt operations on your data projects directly. ## Installation and Setup 1. Install the MCP server 2. Enable it in your client (Claude, Cline, or other MCP-compatible client) ## ⚠️ Important: Project Path Requirement ⚠️ **When using any tool from this MCP server, you MUST specify the fully qualified (absolute) path to your dbt project directory.** ``` # ❌ INCORRECT - will not work { "project_dir": "." } # ✅ CORRECT - will work { "project_dir": "/Users/username/path/to/your/dbt/project" } ``` ### Why this is required: The MCP server runs in its own environment, separate from your client application. When you use relative paths like `.` (current directory), they resolve relative to the server's location, not your project. Providing the full path ensures the server can correctly locate and operate on your dbt project files. ## Available Tools This MCP server provides the following tools for working with dbt: | Tool | Description | Required Parameters | |------|-------------|---------------------| | `dbt_run` | Runs dbt models | `project_dir` (full path) | | `dbt_test` | Runs dbt tests | `project_dir` (full path) | | `dbt_compile` | Compiles dbt models | `project_dir` (full path) | | `dbt_ls` | Lists resources in a dbt project (simplified output by default, full details with `verbose: true`) | `project_dir` (full path) | | `dbt_debug` | Validates project setup | `project_dir` (full path) | | `dbt_deps` | Installs package dependencies | `project_dir` (full path) | | `dbt_seed` | Loads seed data | `project_dir` (full path) | | `dbt_build` | Runs seeds, tests, snapshots, and models | `project_dir` (full path) | | `dbt_show` | Previews results of a model | `models`, `project_dir` (full path) | ## Usage Examples ### Example 1: Running dbt models ```json { "models": "model_name", "project_dir": "/Users/username/dbt_projects/analytics" } ``` ### Example 2: Listing dbt resources #### Simplified output (default) ```json { "resource_type": "model", "project_dir": "/Users/username/dbt_projects/analytics", "output_format": "json" } ``` This returns a simplified JSON with only `name`, `resource_type`, and `depends_on.nodes` for each resource: ```json [ { "name": "customers", "resource_type": "model", "depends_on": { "nodes": ["model.jaffle_shop.stg_customers", "model.jaffle_shop.stg_orders"] } } ] ``` #### Verbose output (full details) ```json { "resource_type": "model", "project_dir": "/Users/username/dbt_projects/analytics", "output_format": "json", "verbose": true } ``` This returns the complete resource information including all configuration details. ### Example 3: Testing dbt models ```json { "models": "my_model", "project_dir": "/Users/username/dbt_projects/analytics" } ``` ## Troubleshooting ### Common Issues 1. **"Project not found" or similar errors** - Make sure you're providing the full absolute path to your dbt project - Check that the path exists and contains a valid dbt_project.yml file 2. **Permissions errors** - Ensure the MCP server has access to the project directory - Check file permissions on your dbt project files 3. **Connection errors** - Verify that your profiles.yml is correctly configured - Check database credentials and connectivity ## Need Help? If you're experiencing issues with the dbt CLI MCP Server, check the documentation or open an issue on the GitHub repository.

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