The Database Insights remote MCP server lets you monitor Cloud SQL for PostgreSQL clusters and instances from your AI-enabled development environments and AI agent platforms by fetching query and system metrics. This document describes how to use the Database Insights remote Model Context Protocol (MCP) server to monitor Cloud SQL for PostgreSQL from AI applications such as Gemini CLI, agent mode in Gemini Code Assist, Claude Code, or in AI applications that you're developing. The Database Insights remote MCP server is enabled when you enable the Database Insights API. [Model Context Protocol](https://modelcontextprotocol.io/docs/getting-started/intro) (MCP) standardizes how large language models (LLMs) and AI applications or agents connect to external data sources. MCP servers let you use their tools, resources, and prompts to take actions and get updated data from their backend service. ## What's the difference between local and remote MCP servers? Local MCP servers : Typically run on your local machine and use the standard input and output streams (stdio) for communication between services on the same device. Remote MCP servers : Run on the service's infrastructure and offer an HTTP endpoint to AI applications for communication between the AI MCP client and the MCP server. For more information about MCP architecture, see [MCP architecture](https://modelcontextprotocol.io/docs/learn/architecture). ## Stateless core With [MCP version 2026-07-28](https://modelcontextprotocol.io/specification/2026-07-28), MCP changes from a bidirectional, stateful protocol to a stateless protocol. Each MCP request is self-describing and can be routed using headers. There isn't a need for the `initialize`/`initialized` handshake or `Mcp-Session-Id` because each request includes all the information needed in HTTP headers or the `_meta` parameter. MCP servers can request additional information required by a tool through [multi-round-trip requests (MRTR)](https://modelcontextprotocol.io/specification/latest/basic/patterns/mrtr). To help route and process requests without parsing the request body, some MCP headers are required, including the following: - Headers that are required by the MCP specification such as the [protocol version header](https://modelcontextprotocol.io/specification/latest/basic/transports/streamable-http#protocol-version-header) and [standard request headers](https://modelcontextprotocol.io/specification/latest/basic/transports/streamable-http#standard-request-headers). - [Custom headers](https://modelcontextprotocol.io/specification/latest/basic/transports/streamable-http#custom-headers-from-tool-parameters) that are defined by the MCP server. These headers are mirrored into HTTP headers from the tool's input schema using the `x-mcp-header` property. For example, an MCP server might define a custom header to specify the Google Cloud region or project ID. For more information about MCP architecture, see the MCP version 2026-07-28 [specification](https://modelcontextprotocol.io/specification/2026-07-28) and [key changes](https://modelcontextprotocol.io/specification/2026-07-28/changelog). ## Google and Google Cloud remote MCP servers Google and Google Cloud remote MCP servers have the following features and benefits:
- Simplified, centralized discovery - Managed global or regional HTTP endpoints - Fine-grained authorization - Optional prompt and response security with Model Armor protection - Centralized audit logging For information about other MCP servers and information about security and governance controls available for Google Cloud MCP servers, see [Google Cloud MCP servers overview](https://docs.cloud.google.com/mcp/overview.md.txt). ## Before you begin
### Required roles To get the permissions that you need to use the Database Insights MCP server, ask your administrator to grant you the following IAM roles on the project where you want to use the Database Insights MCP server: - Make MCP tool calls: [MCP Tool User](https://docs.cloud.google.com/iam/docs/roles-permissions/mcp.md.txt#mcp.toolUser) (`roles/mcp.toolUser`) - View Cloud Monitoring data: [Monitoring Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/monitoring.md.txt#monitoring.viewer) (`roles/monitoring.viewer`) - View Database Insights data: [Database Insights Viewer](https://docs.cloud.google.com/iam/docs/roles-permissions/databaseinsights.md.txt#databaseinsights.viewer) (`roles/databaseinsights.viewer`) For more information about granting roles, see [Manage access to projects, folders, and organizations](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access.md.txt). These predefined roles contain the permissions required to use the Database Insights MCP server. To see the exact permissions that are required, expand the **Required permissions** section: #### Required permissions The following permissions are required to use the Database Insights MCP server: - Make MCP tool calls: `mcp.tools.call` - Get query metrics: `queryMetrics.fetch` - Get system metrics: `systemMetrics.fetch` - View Monitoring metrics: `monitoring.timeseries.list` You might also be able to get these permissions with [custom roles](https://docs.cloud.google.com/iam/docs/creating-custom-roles.md.txt) or other [predefined roles](https://docs.cloud.google.com/iam/docs/roles-overview.md.txt#predefined). ## Authentication and authorization The Database Insights remote MCP server uses the [OAuth 2.0](https://developers.google.com/identity/protocols/oauth2) protocol with [Identity and Access Management (IAM)](https://docs.cloud.google.com/iam/docs/overview.md.txt) for authentication and authorization. All [Google Cloud identities](https://docs.cloud.google.com/docs/authentication/identity-products.md.txt) are supported for authentication to MCP servers. The Database Insights remote MCP server doesn't accept API keys. We recommend that you create a separate identity for agents that are using MCP tools so that access to resources can be controlled and monitored. For more information about authentication, see [Authenticate to MCP servers](https://docs.cloud.google.com/mcp/authenticate-mcp.md.txt). ## Database Insights MCP OAuth scopes Database Insights has the following MCP tool OAuth scopes: | Scope URI for gcloud CLI | Description | |---|---| | `https://www.googleapis.com/auth/cloud-platform` | Queries and analyzes database performance and system metrics. | Additional scopes might be required on the resources accessed during a tool call. ## Configure an MCP client to use the Database Insights MCP server AI applications and agents, such as Claude or Antigravity, can instantiate an MCP client that connects to a single MCP server. An AI application can have multiple clients that connect to different MCP servers. If your application isn't listed in the [client-specific guidance](https://docs.cloud.google.com/mcp/configure-mcp-ai-application.md.txt#client-specific-guidance), then you can use the following information to connect from most applications. In your AI application, look for a way to add or connect to a remote MCP server. For the Database Insights MCP server, enter the following as required: - **Server name**: Database Insights MCP server - **Server URL** or **Endpoint** : `https://databaseinsights.googleapis.com/mcp` - **Transport** : [Streamable HTTP](https://modelcontextprotocol.io/specification/latest/basic/transports/streamable-http) - **Authentication details** : Depending on how you want to authenticate, you can enter your Google Cloud credentials, your OAuth Client ID and secret, or an agent identity and credentials. For more information about authentication, see [Authenticate to MCP servers](https://docs.cloud.google.com/mcp/authenticate-mcp.md.txt). - **OAuth scope** : the [OAuth 2.0 scope](https://developers.google.com/identity/protocols/oauth2/scopes) that you want to use when connecting to the Database Insights MCP server. For application-specific guidance about setting up and connecting to MCP server, see [Client-specific guidance](https://docs.cloud.google.com/mcp/configure-mcp-ai-application.md.txt#client-specific-guidance). For more general guidance, see the following resources: - [Connect to remote MCP servers](https://modelcontextprotocol.io/docs/develop/connect-remote-servers). - [Configure MCP in an AI application](https://docs.cloud.google.com/mcp/configure-mcp-ai-application.md.txt). ## Available tools To view details of available MCP tools and their descriptions for the Database Insights MCP server, see the [Database Insights MCP reference](https://docs.cloud.google.com/sql/docs/postgres/reference/mcp/databaseinsights/mcp.md.txt). ### List tools Use the [MCP inspector](https://modelcontextprotocol.io/docs/tools/inspector) to list tools, or send a `tools/list` HTTP request directly to the Database Insights remote MCP server. The `tools/list` method doesn't require authentication. curl -X POST https://databaseinsights.googleapis.com/TOOLSET_ENDPOINT \ -H 'Content-Type: application/json' \ -H 'Accept: application/json' \ -H 'MCP-Protocol-Version: MCP_PROTOCOL_VERSION' \ -H 'Mcp-Method: tools/list' \ -d '{ "jsonrpc": "2.0", "id": 1, "method": "tools/list", "params": { "_meta": { "io.modelcontextprotocol/protocolVersion": "MCP_PROTOCOL_VERSION", "io.modelcontextprotocol/clientCapabilities": { "extensions": { "io.modelcontextprotocol/ui": { "mimeTypes": ["text/html;profile=mcp-app"] } } } } } }' Replace the following: - `TOOLSET_ENDPOINT`: The remainder of the MCP endpoint after the service name. For example, for Database Insights, this might be `mcp/toolset-name`. - `MCP_PROTOCOL_VERSION`: The MCP protocol version. For example, `2026-07-28`. ## Sample use cases The following are sample use cases for monitoring Cloud SQL for PostgreSQL using the Database Insights MCP server. ### Monitor query performance You can use the Database Insights MCP server to identify slow queries and understand database workload patterns. **Sample prompt:** "Find the top 5 queries with the highest execution time on my Cloud SQL for PostgreSQL instance in project PROJECT_ID over the last hour." **Workflow:** The workflow for monitoring query performance includes the following steps: - **Data fetching** : The agent calls the `get_query_metrics` tool with a PromQL query configured to fetch `cloudsql.googleapis.com/database/postgresql/insights/aggregate/execution_time`. - **Analysis**: The agent processes the returned time-series data to identify the queries with the highest accumulated execution time. - **Reporting**: The agent lists the query hashes and their respective execution times, helping you identify potential bottlenecks. ### System health check You can monitor the resource utilization of your Cloud SQL for PostgreSQL instances to ensure they are sized correctly and performing optimally. **Sample prompt**: "What has been the average CPU utilization and available memory for my Cloud SQL for PostgreSQL instance INSTANCE_ID over the past 24 hours?" **Workflow**: The workflow for a system health check includes the following steps: - **Metric retrieval** : The agent uses the `get_system_metrics` tool to fetch `cloudsql.googleapis.com/database/cpu/utilization` for the specified instance. - **Summarization**: The agent aggregates the data over the 24-hour period. - **Reporting**: The agent provides a summary of the CPU and memory trends, alerting you if utilization peaked near the limits. ## Optional security and safety configurations MCP introduces new security risks and considerations due to the wide variety of actions that you can do with the MCP tools. To minimize and manage these risks, Google Cloud offers default settings and customizable policies to control the use of MCP tools in your Google Cloud organization or project. For more information about MCP security and governance, see [AI security and safety](https://docs.cloud.google.com/mcp/ai-security-safety.md.txt). ### Use Model Armor [Model Armor](https://docs.cloud.google.com/model-armor/overview.md.txt) is a Google Cloud service designed to enhance the security and safety of your AI applications. It works by proactively screening LLM prompts and responses, protecting against various risks and supporting responsible AI practices. Whether you are deploying AI in your cloud environment, or on external cloud providers, Model Armor can help you prevent malicious input, verify content safety, protect sensitive data, maintain compliance, and enforce your AI safety and security policies consistently across your diverse AI landscape. When Model Armor is enabled with [logging enabled](https://docs.cloud.google.com/model-armor/configure-logging.md.txt), Model Armor logs the entire payload. This might expose sensitive information in your logs. #### MCP request routing to Model Armor Model Armor is available in [certain regions](https://docs.cloud.google.com/model-armor/locations.md.txt). When Model Armor is enabled and you use an MCP server in a jurisdiction that Model Armor doesn't support, the routing behavior of the call might be different for different MCP servers and might break data residency compliance for in-use and in-transit data. For more information about the behavior of individual MCP servers, see [Model Armor supported products](https://docs.cloud.google.com/mcp/model-armor-supported-products.md.txt). #### Enable Model Armor You must enable Model Armor APIs before you can use Model Armor. ### Console 1. Enable the Model Armor API, if it is not already enabled. **Roles required to enable APIs** To enable APIs, you need the `serviceusage.services.enable` permission. If you created the project, then you likely already have this permission through the Owner role (`roles/owner`). Otherwise, you can get this permission through the Service Usage Admin role (`roles/serviceusage.serviceUsageAdmin`). [Learn how to grant roles](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access.md.txt). [Enable the API](https://console.cloud.google.com/apis/enableflow?apiid=modelarmor.googleapis.com)
2. Select the project where you want to activate Model Armor. ### gcloud Before you begin, follow these steps using the [gcloud CLI](https://docs.cloud.google.com/sdk/gcloud.md.txt) with the Model Armor API: 1. In the Google Cloud console, activate Cloud Shell. [Activate Cloud Shell](https://console.cloud.google.com/?cloudshell=true) At the bottom of the Google Cloud console, a [Cloud Shell](https://docs.cloud.google.com/shell/docs/how-cloud-shell-works.md.txt) session starts and displays a command-line prompt. Cloud Shell is a shell environment with the [gcloud CLI](https://docs.cloud.google.com/sdk/gcloud.md.txt) already installed and with values already set for your current project. It can take a few seconds for the session to initialize.
2. Run the following command to set the API endpoint for the Model Armor service. ```bash gcloud config set api_endpoint_overrides/modelarmor "https://modelarmor.LOCATION.rep.googleapis.com/" ``` Replace `LOCATION` with the region where you want to use Model Armor.
#### Configure protection for Google and Google Cloud remote MCP servers To help protect your MCP tool calls and responses you can use Model Armor floor settings. A floor setting defines the minimum security filters that apply across the project. This configuration applies a consistent set of filters to all MCP tool calls and responses within the project. > [!TIP] > **Tip:** Don't enable the prompt injection and jailbreak filter unless your MCP traffic carries natural language data. Set up a Model Armor floor setting with MCP sanitization enabled. For more information, see [Configure Model Armor floor settings](https://docs.cloud.google.com/model-armor/configure-floor-settings). > [!NOTE] > **Note:** If the agent and the MCP server are in different projects, you can create floor settings in both projects (the client project and the resource project). In this case, Model Armor is invoked twice, once for each project. See the following example command: ```bash gcloud model-armor floorsettings update \ --full-uri='projects/PROJECT_ID/locations/global/floorSetting' \ --enable-floor-setting-enforcement=TRUE \ --add-integrated-services=GOOGLE_MCP_SERVER \ --google-mcp-server-enforcement-type=INSPECT_AND_BLOCK \ --enable-google-mcp-server-cloud-logging \ --malicious-uri-filter-settings-enforcement=ENABLED \ --add-rai-settings-filters='[{"confidenceLevel": "MEDIUM_AND_ABOVE", "filterType": "DANGEROUS"}]' ``` Replace `PROJECT_ID` with your Google Cloud project ID. Note the following settings: - `INSPECT_AND_BLOCK`: The enforcement type that inspects content for the Google MCP server and blocks prompts and responses that match the filters. - `ENABLED`: The setting that enables a filter or enforcement. - `MEDIUM_AND_ABOVE`: The confidence level for the Responsible AI - Dangerous filter settings. You can modify this setting, though lower values might result in more false positives. For more information, see [Model Armor confidence levels](https://docs.cloud.google.com/model-armor/overview.md.txt#ma-confidence-levels). #### Disable scanning MCP traffic with Model Armor To stop Model Armor from automatically scanning traffic to and from Google MCP servers based on the project's floor settings, run the following command: ```bash gcloud model-armor floorsettings update \ --full-uri='projects/PROJECT_ID/locations/global/floorSetting' \ --remove-integrated-services=GOOGLE_MCP_SERVER ``` Replace `PROJECT_ID` with the Google Cloud project ID. Model Armor doesn't automatically apply the rules defined in this project's floor settings to any Google MCP server traffic. Model Armor floor settings and general configuration can impact more than just MCP. Because Model Armor integrates with services like Vertex AI, any changes you make to floor settings can affect traffic scanning and safety behaviors across all integrated services, not just MCP. ### Control MCP use with IAM policies Identity and Access Management (IAM) [deny policies](https://docs.cloud.google.com/iam/docs/deny-overview.md.txt) and [allow policies](https://docs.cloud.google.com/iam/docs/allow-policies.md.txt) help you secure Google Cloud and Google MCP servers. You can combine multiple criteria to build customized security and governance policies by allowing or denying access based on the following: - The principal. - Tool properties like the read-only attribute. - The service name or tool name. - The application's OAuth client ID. For more information, see [Control MCP use with Identity and Access Management](https://docs.cloud.google.com/mcp/control-mcp-use-iam.md.txt). ## What's next - Read the [Database Insights MCP reference documentation](https://docs.cloud.google.com/sql/docs/postgres/reference/mcp/databaseinsights/mcp.md.txt). - Learn more about [Google Cloud MCP servers](https://docs.cloud.google.com/mcp/overview.md.txt).