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Connect to dbt projects via MCP for analysis, model understanding, and insights.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "dbt-core-mcp" yet — see the docs or source repo.
Connect to this dbt project, list the core models and their upstream/downstream dependencies, and summarize the business meaning of each model in English.
A list of key models, dependency explanations, and a summary of each model's business purpose.
Using the dbt project, analyze which models, fields, and calculation logic may explain a drop in order conversion rate, and suggest the best investigation path.
Relevant model and field lineage, possible anomaly points, and a recommended troubleshooting order.
Review the dbt project's model structure and create a quick-start guide for new team members, including subject areas, core tables, and common analysis entry points.
A structured onboarding note that helps new members understand the project's data structure and analysis paths.
Interact with the dbt ecosystem for modeling, semantic queries, and metadata discovery.
Connect to Jupyter via MCP to run code and explore data interactively.
Safely explore, profile, and query SQLite and PostgreSQL databases with AI.
Connect LLM apps to query and manage multiple databases through one tool.
Inspect database schemas, index issues, table bloat, and query plans.
Connect to Microsoft SQL Server for queries, analysis, and visualization generation.