Struggling to connect your AI agents to your valuable enterprise data? Building powerful AI applications that truly understand your data, respond to natural language, and dynamically select the right tools can feel like navigating a complex maze. Data silos and security concerns often stand in the way. Enter MCP Toolbox for Databases! This open-source MCP (Model Context Protocol) server is your streamlined solution to bridge AI and databases – making connections easier, faster, and more secure. Think of MCP as the universal adapter, standardizing how AI agents communicate with essential tools. Why choose MCP Toolbox? Simplified Development: Integrate tools into your AI agents with minimal code. Better Performance: Benefit from out-of-the-box best practices like connection pooling. Enhanced Security: Leverage integrated authentication methods for robust data protection. End-to-End Observability: Gain clear insights with built-in OpenTelemetry for metrics and tracing. Centralized Tool Management: Manage and share tools efficiently between agents and applications. How does it work? MCP Toolbox for Databases acts as a control plane between your AI agent’s orchestration framework (like Google’s Agent Development Kit) and your databases. Your AI agent (the MCP client) connects to the Toolbox (the MCP server) to access defined database capabilities. See it in Action: Time Series Forecasting Agent! Imagine asking your AI: "Forecast liquor sales for next week in Iowa." This agent, potentially powered by a Large Language Model like Gemini, uses MCP Toolbox to understand the request and dynamically select the right forecasting tool defined in a simple tools.yaml configuration file. This example, available in the GitHub ADK samples repository, showcases how to use BigQuery’s AI.FORECAST function with the state-of-the-art TimesFM model. The beauty lies in the dynamic tool system – update tools.yaml, restart the Toolbox, and your agent has new capabilities without touching its core code! This agentic design cleverly combines the broad understanding of LLMs like Gemini with the specialized precision of models like TimesFM for accurate forecasting. Broad Support & Seamless Integration: MCP Toolbox for Databases supports numerous Google Cloud databases (AlloyDB, Spanner, Cloud SQL, Bigtable, etc.) and includes contributions for third-party databases like Neo4j and Dgraph. Easily integrate these tools into your applications with our client SDKs and ready-made integrations for popular frameworks like LangGraph and LlamaIndex.
Resources:
Explore MCP Toolbox for Databases on GitHub→https://goo.gle/3Sm4BgJ
Dive into the Documentation →https://goo.gle/3SssRhb
Check out the Agent Development Kit (ADK) Samples (including the Time Series Forecasting Agent) → https://goo.gle/3SrXHXk
Learn more about BigQuery AI.FORECAST → https://goo.gle/4kGcKJ0
Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
Speakers: Karl Weinmeister
