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Connecting an LLM to a database is the "Hello World" of agentic AI, but scaling that to production requires solving complex problems in security, context management, and reliability. You can't simply feed a 500-table schema into a context window and hope for the best. In this session, the creators of the MCP Toolbox for Databases (12.5k stars) break down the specific architecture required to give agents safe, high-fidelity access to your data. You will learn the patterns that power over 6 million monthly tool calls, including: Raw SQL vs. Semantic Abstraction: A framework for deciding when to give an agent raw query power vs. when to abstract logic into strict semantic tools. Safety & Governance: Implementing read-only guardrails, query validation, and "Human-in-the-Loop" friction points to prevent accidental data loss or injection risks. Reducing Hallucinations: How to format database metadata and column descriptions to drastically improve an agent's query accuracy.
Kurtis Van Gent is a MCP Core Maintainer and leads the MCP Transports Working Group. By day, he leads AI Ecosystems + Integrations for Google Cloud Databases and helped create MCP Toolbox for Databases.
Wenxin Du is a core maintainer of MCP Toolbox for Databases. She delivered the end-to-end implementation of Toolbox's end-user authorization system and integrated semantic search functionality into Toolbox.
