Build Data-Aware AI Agents| MCP Toolbox for Databases Course
In earlier episodes, we explored:
- The Data Gap — why most AI agents fall short
- How connecting agents to live databases transforms them
Now, it’s time to build agents that actually know something.
This video introduces the MCP Toolbox for Databases, letting you connect your AI agents to BigQuery via an MCP server — no manual integration needed.
What is MCP Toolbox for Databases?
In this hands-on tutorial, we walk through a practical 6-step framework to connect your AI agents to BigQuery using the MCP Toolbox for Databases. This mirrors a pattern I’ve shared at multiple Google Cloud Community Days to help developers go from prompt-based agents to data-aware copilots.
We also reference and build upon the official Google Cloud Codelab — which provides a great guided walkthrough of connecting MCP Toolbox to a BigQuery dataset. You can follow along using your own schema or adapt the example used in the Codelab.
With MCP Toolbox, you can:
- Define tools in YAML instead of backend code
- Write parameterized SQL queries for BigQuery
- Securely package queries into toolsets
- Let ADK agents call these tools directly (Gemini function calling)
It’s how you give your agent access to live business data.
What You’ll Build in This Episode
In this hands-on tutorial, we walk through a practical 6-step framework to connect your AI agents to BigQuery using the MCP Toolbox for Databases. This mirrors a pattern I’ve shared at multiple Google Cloud Community Days to help developers go from prompt-based agents to data-aware copilots.
We’ll use a real-world example: querying the public Google Cloud Release Notes BigQuery dataset. This hands-on tutorial will cover:
- How to configure your `tools.yaml` file and launching the MCP Toolbox server.
- The essential steps to connect your ADK agent to this locally running MCP Toolbox server.
Why This Matters
This is the moment your agents become useful.
Before this episode, your agents could respond — but not reason with data. After this, they can generate answers based on real sets of inventory or sales data.
Think:
- “How many widgets are left in stock?”
- “Which product sold the most this month?”
- “Any products need restocking?”
No hallucination, just facts.
Watch Video 4: Build Data-Aware AI Agents with MCP Toolbox & ADK | Your First MCP Server
Coming Next…
In the next tutorial, we build two real agents using this toolset:
- An Inventory Management Agent
- A Sales Data Co‑Pilot
These agents will use parameterized tools and live data to answer real-world queries — no backend required.
Additional Links:
- Watch Video 1-Introduction
- Watch Video 2 — The Data Gap
- Watch Video 3 — Building your First AI Agent with ADK
- Course Introduction Blog
- Build a Google Tasks To-Do Agent using Agent Development Kit
🔗 Access the Course GitHub Repository Here
