Source URL: https://cloud.google.com/blog/products/ai-machine-learning/mcp-toolbox-for-databases-now-supports-model-context-protocol/
Source: Cloud Blog
Title: MCP Toolbox for Databases: Simplify AI Agent Access to Enterprise Data
Feedly Summary: At Google Cloud Next 25, we announced incredible ways for enterprises to build multi-agent ecosystems with Vertex AI and Google Cloud Databases – including better ways for agents to communicate with each other using Agent2Agent Protocol and Model Context Protocol (MCP). With the growing excitement around MCP for developers, we’re making it easy for MCP Toolbox for Databases (formerly Gen AI Toolbox for Databases) to access your enterprise data in databases. This is another step forward in providing secure and standardized ways to innovate with agentic applications. Let’s take a look.
MCP Toolbox for Databases (formerly Gen AI Toolbox for Databases)
MCP Toolbox for Databases (Toolbox) is an open-source MCP (Model Context Protocol) server that allows developers to connect gen AI agents to enterprise data easily and securely. MCP is an emerging open standard created by Anthropic for connecting AI systems with data sources through a standardized protocol, replacing fragmented integrations that require custom integrations.
Currently, Toolbox can be used to build tools for a large number of databases: AlloyDB for PostgreSQL (including AlloyDB Omni), Spanner, Cloud SQL for PostgreSQL, Cloud SQL for MySQL, Cloud SQL for SQL Server, and self-managed MySQL and PostgreSQL. Because it’s fully open-source, it includes contributions from third-party databases such as Neo4j and Dgraph. Toolbox offers simplified development with reduced boilerplate code, enhanced security through OAuth2 and OIDC, and end-to-end observability with OpenTelemetry integration. This enables you to develop tools easier, faster, and more securely by handling the complexities such as connection pooling, authentication, and more.
As an MCP server, Toolbox provides the additional scaffolding for implementing production-quality database tools and making them accessible to any client in the growing MCP ecosystem. This compatibility allows developers building agentic applications to leverage Toolbox and securely query a wide range of databases through a single, standardized protocol, simplifying development and enhancing interoperability.
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MCP Toolbox for Databases supports Agent Development Kit (ADK)
At Next, we launched the Agent Development Kit (ADK), an open-source framework that simplifies the process of building sophisticated multi-agent systems while maintaining precise control over agent behavior. With ADK, you can build an AI agent in under 100 lines of intuitive code. With ADK, you can:
Shape how your agents think, reason, and collaborate through deterministic guardrails and orchestration controls.
Interact with your agents in human-like conversations with ADK’s unique bidirectional audio and video streaming capabilities enabled with just a few lines of code. Check out the demo of an interactive agent from the opening keynote at NEXT 2025 built on the ADK here.
Choose the model or deployment that works best for your needs. ADK works with your stack of choice – whether that’s your preferred top-tier model, deployment target, or integration with remote agents built on other frameworks. ADK also supports the Model Context Protocol (MCP), enabling secure, two-way connections between your data sources and AI agents.
Deploy to production using the direct integration to Vertex AI Agent Engine. This clear and reliable path from development to enterprise-grade deployment eliminates the typical overhead associated with moving agents into production.
Diagram showing Toolbox with support for ADK and connecting to databases
To get started, go to Vertex AI Agent Garden to explore a curated set of agent samples for common use cases like data science and customer service agents. Discover tools that can be easily used to build agents with ADK such as connecting agents to databases with the integrated MCP Toolbox for Databases. You can access source code in GitHub samples that you can clone and start using to develop your own agents.
Adding LangGraph support
LangGraph gives you essential built-in support for persistence layer, implemented through checkpointers. This helps you build resilient, stateful agents that can reliably manage long-running tasks or resume after interruptions.
To leverage powerful managed databases for storing this state, Google Cloud offers dedicated integration libraries. Developers can choose the following:
The highly scalable AlloyDB for PostgreSQL using the AlloyDBSaver class from the langchain-google-alloydb-pg-python library, or opt for
Cloud SQL for PostgreSQL utilizing the corresponding checkpointer implementation, PostgresSaver, within the langchain-google-cloud-sql-pg-python library.
Both offer robust mechanisms to seamlessly save and load agent execution states, allowing workflows to be reliably paused, resumed, and audited, backed by the manageability and performance of Google Cloud’s PostgreSQL offerings.
When you compile graph with a checkpointer, the checkpointer saves a checkpoint of the graph state at every super-step. Those checkpoints are saved to a thread, which can be accessed after graph execution. Because threads allow access to graph’s state after execution, several powerful capabilities including human-in-the-loop, memory, time travel, and fault-tolerance are all possible.
Install the packages:
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Learn more about langgraph checkpoint usage for AlloyDB here and Cloud SQL PG here.
Get started
This Colab demonstrates a complete workflow for building and deploying a LangGraph Hotel Agent which can search, book and cancel hotels. This sample shows how to build and deploy an agent (model, tools, and reasoning) using the Vertex AI SDK and MCP Toolbox for Databases.
The demonstration will begin with agent development, integrating the MCP Toolbox for Databases to Search, Book, and Cancel hotels. It will then walk you through deploying the agent to Agent Engine and the MCP Toolbox to Cloud Run, and conclude by demonstrating how to connect these services remotely.
Here are some more resources to get started with Toolbox and MCP.
MCP Quick Start
How to connect to Toolbox from MCP Client
MCP Introduction documentation
MCP github org
MCP spec
AI Summary and Description: Yes
Summary: The text outlines the advancements introduced at Google Cloud Next 25, particularly focusing on the Vertex AI and the Model Context Protocol (MCP). It highlights new methods for creating multi-agent ecosystems, improved database connections for enterprise applications, and enhanced tools for developing AI agents, emphasizing security, streamlined development, and interoperability.
Detailed Description:
The announcement from Google Cloud Next 25 showcases innovative tools for enterprises looking to utilize artificial intelligence effectively within cloud environments. Key points include:
– **MCP Toolbox for Databases**:
– An open-source server facilitating the connection between generative AI agents and enterprise data.
– Implements the Model Context Protocol (MCP) designed by Anthropic, providing a secure and standardized method to connect AI systems with various data sources.
– Supports a wide range of databases, including:
– AlloyDB for PostgreSQL
– Spanner
– Cloud SQL for PostgreSQL, MySQL, and SQL Server
– The Toolbox enhances security using OAuth2 and OpenID Connect (OIDC) for authentication and includes observability features via OpenTelemetry.
– Simplifies development by managing complexities like connection pooling and authentication, allowing developers to focus on building applications rather than on infrastructure challenges.
– **Agent Development Kit (ADK)**:
– A framework that enables the quick creation of advanced multi-agent systems with a focus on controlling agent behaviors.
– Simplifies the coding process, enabling the development of sophisticated AI agents within less than 100 lines of code.
– Features include human-like interactions via audio and video streaming and seamless integration with existing data sources through MCP.
– **LangGraph Support**:
– Introduces functionalities to manage the state of AI agents, such as persistent checkpoints allowing for the resumption of long-running tasks.
– Offers integration with highly scalable database options like AlloyDB and Cloud SQL, enhancing agent reliability and execution management.
– **Deployment and Accessibility**:
– The new tools are integrated with Vertex AI, allowing for straightforward development-to-production workflows.
– Offers community resources and examples to facilitate learning and practical implementation.
Overall, the announcements signal a notable stride toward enhancing efficiency, security, and usability in building AI-driven applications within enterprise frameworks. Security professionals will find the focus on OAuth2 and OIDC particularly compelling, as it aligns with broader security trends emphasizing strong authentication protocols. The open-source nature of the MCP Toolbox for Databases and ADK fosters community-driven improvements and innovations, which can lead to faster adoption and incremental enhancements in AI application development.