For developers building AI applications, including custom agents and chatbots, the open-source Model Context Protocol (MCP) standard enables your innovations to access data and tools consistently and securely. At the end of 2025, we introduced managed and remote MCP support for services like Google Maps and BigQuery, establishing a standard method for AI to connect with tools, and effectively creating a universal interface for applications. Today, we are expanding this offering to include PostgreSQL with AlloyDB, Spanner and Cloud SQL, as well as Firestore and Bigtable for high-performance NoSQL workloads, and introducing a new Developer Knowledge MCP server, which presents an API to connect IDEs to Google’s documentation. These servers run in Google Cloud, providing a secure interface for Gemini and other MCP-compliant clients to easily interact with data and infrastructure.
With the launch of Gemini 3, developers gained advanced reasoning capabilities to plan, build, and solve complex problems. But for an AI model to function as a useful “agent,” it must reliably interact with its environment. Today’s announcement extends these capabilities more broadly to the database tools our customers leverage daily as the backbone of their work environment.
To connect your agents to these servers, you don’t need to deploy infrastructure. Just configure the MCP server endpoint in the agent configuration and immediately gain access to your operational data, backed by enterprise-grade auditing, observability and governance. With no infrastructure management, you can scale your agentic workloads without incurring operational overhead.
Bringing operational data to agents
These new managed servers enable agents to access specific capabilities across our portfolio:
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AlloyDB for PostgreSQL: Agents can interact with PostgreSQL workloads, enabling tasks such as schema creation, diagnosing complex queries for slowness and performing vector similarity search.
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Spanner: With unified multi-model capabilities in Spanner such as Spanner Graph, agents can model and query complex relationships directly alongside relational and semantic data using standard (SQL and GQL) queries. This allows agents to quickly uncover deep insights (like identifying fraud rings or generating product recommendations) using the MCP tools at its disposal.
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Cloud SQL for PostgreSQL, MySQL and SQL Server: Developers and database administrators can use the Cloud SQL MCP Server across MySQL, PostgreSQL, and SQL Server fleets for natural language interactions with the database, AI-assisted app development, query performance optimization and database troubleshooting via agents.
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Bigtable: Bigtable’s flexible schema and high-throughput ingestion capabilities are commonly used for building digital integration hubs and managing time series data. MCP simplifies automating operational workflows and developing agentic customer support, CRM, human resources, IT operations, supply chain and logistics applications with this data.
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Firestore: Focused on mobile and web development, the Firestore MCP server enables agents to sync with live document collections. This supports dynamic interactions such as checking user session states or verifying order statuses via natural language prompts.
Managing applications and infrastructure
Beyond data retrieval, we are enabling agents to help build and manage applications. The Developer Knowledge MCP server connects IDEs to Google’s documentation, allowing agents to answer technical questions and troubleshoot code with relevant context.
Security and governance
Connecting an agent to a database requires robust security and governance. These servers are built on Google Cloud’s standard identity and observability frameworks:
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Identity-first security: Authentication is handled entirely through Identity and Access Management (IAM) rather than shared keys. This ensures agents can only access the specific tables or views explicitly authorized by the user.
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Full observability: To track agent activity, every query and action taken via these MCP servers is logged in Cloud Audit Logs. This provides security teams with a record of every database interaction, maintaining visibility alongside ease of access.
Demo: From local code to managed data
Let’s see these new MCP servers in action.
Imagine an agent designed to automate the migration of a full-stack event management platform for fitness communities. Through a series of natural language instructions in the Gemini CLI, the agent utilizes the Cloud SQL remote MCP server to provision a managed PostgreSQL instance, apply the correct schema, and securely migrate your local data. You don’t need to master complex gcloud commands or become a Cloud SQL expert; the agent handles the heavy lifting. This transition is architected in real-time by the Developer Knowledge MCP server, which references official documentation to guide the agent through best practices — easily upgrading your application’s backbone from local storage to a fully managed enterprise database.
