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5.4 - Using Bruin MCP with AI Agents

What is Bruin MCP?

MCP stands for Model Context Protocol. Bruin MCP is a way for AI agents (in Cursor, VS Code, Claude, etc.) to communicate with Bruin — querying documentation, running commands on your behalf, going through your code, troubleshooting, and analyzing data.

With the Bruin MCP and an AI agent, you can:

  • Write pipeline code and asset configurations
  • Write documentation and metadata
  • Troubleshoot errors and debug issues
  • Run queries and analyze data using natural language
  • Ask questions about your pipeline logic and structure

Installing Bruin MCP

Make sure you have Bruin CLI installed first.

Cursor

Go to Settings → Tools & MCP → New MCP Server and add:

{
  "mcpServers": {
    "bruin": {
      "command": "bruin",
      "args": ["mcp"]
    }
  }
}

If it shows a failure/error, close and reopen your IDE — you should see "Bruin enabled".

VS Code (Copilot)

Create .vscode/mcp.json in your project folder:

{
  "servers": {
    "bruin": {
      "command": "bruin",
      "args": ["mcp"]
    }
  }
}

Claude Code

claude mcp add bruin -- bruin mcp

See the full Bruin MCP documentation for other agents and troubleshooting.

Building a pipeline with MCP

Using the template prompt

The zoomcamp template includes an example prompt in its README that you can give to the AI agent to create the entire pipeline end-to-end:

bruin init zoomcamp my-taxi-pipeline

Open the generated README.md — it contains a prompt you can paste into the agent to scaffold the entire pipeline automatically.

What the agent does

When given the pipeline prompt, the agent will:

  1. Create all pipeline assets (ingestion, staging, reports)
  2. Configure materialization strategies and dependencies
  3. Set up quality checks and column metadata
  4. Validate the pipeline with bruin validate
  5. Run the pipeline with a test date range
  6. Run custom checks to validate query logic
  7. Execute verification queries using bruin query

Working incrementally

In practice, you may prefer working asset by asset rather than generating everything at once. This lets you be involved in every design choice:

  • Create and test the ingestion asset first
  • Then build the staging layer
  • Then add the reports layer
  • Review and adjust quality checks at each step

Querying data with the agent

Once your pipeline has run, you can use the agent conversationally to query your data:

Example queries:

  • "Query the staging table and tell me how many days of data we have"
  • "Which day had the highest number of trips and total fare?"
  • "In which asset are we aggregating data?"

The agent understands the context of your pipeline — it knows the table structures, can write SQL queries, and can explain the logic behind each asset. This is useful for:

  • Ad hoc analysis without writing SQL manually
  • Understanding unfamiliar pipeline logic
  • Data validation and troubleshooting
  • Onboarding new team members to an existing pipeline