The Memorystore for Valkey managed MCP extension lets you manage Memorystore for Valkey instances and backups from your AI-enabled development environments and AI agent platforms.

Google and Google Cloud managed MCP servers can be used in your AI applications with enterprise-ready governance, security, and access control.

1. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

2. Get your administrator to grant you the MCP Tool User role (roles/mcp.toolUser) on the Google Cloud project. If you created a new project, then you already have the required permissions.

3. Ensure your administrator has enabled the Memorystore for Valkey API on the Google Cloud project.

This extension uses Google Application Default Credentials (ADC) to perform authentication. To login with ADC, run the following command in your terminal:

gcloud auth application-default login

For additional details, see the ADC documentation.

To install the extension, run the following command in your terminal:

gemini extensions install https://github.com/gemini-cli-extensions/memorystore

To see a complete list of available tools and their schemas, see the Memorystore for Valkey MCP reference.

The following are sample use cases for the Memorystore for Valkey MCP server:

• "Why do you create a Memorystore for Valkey regional instance with IAM authentication enabled?"

  Creating this type of instance eliminates static passwords in favor of centralized, short-lived credentials for highly secure, regional workloads. The AI agent of the Memorystore for Valkey MCP server uses the create_instance MCP tool to create the instance.

• "Why do you view all active Memorystore for Valkey instances in a specific region?"

  By listing these instances, you can ensure that resources match your current architecture. The AI agent of the Memorystore for Valkey MCP server uses the list_instances MCP tool to retrieve a formatted list of instances in the specified region.

• "Why do you retrieve connection endpoints and operational metadata from a Memorystore for Valkey instance in a specific region?"

  You need this information for application integration and system maintenance. The AI agent of the Memorystore for Valkey MCP server uses the get_instance MCP tool to retrieve information about the instance, such as its discovery endpoint, shard count, and replica count.

• "How can you optimize Memorystore for Valkey for your data-intensive applications?"

  To increase both the CPU capacity and the memory throughput for these applications significantly, you can scale a Memorystore for Valkey instance by increasing the instance's shard count. The AI agent of the Memorystore for Valkey MCP server uses the update_instance MCP tool to update the shard count for the instance.

• "How can you protect your data from failures that might occur from either a Memorystore for Valkey instance or the region where it's located?"

  Create a backup of the Memorystore for Valkey instance. If a regional or instance failure occurs, then you can restore your data to a new instance to resume operations. The AI agent of the Memorystore for Valkey MCP server uses the backup_instance MCP tool to create a backup of the instance.

MCP introduces new security risks and considerations due to the wide variety of actions that you can take with MCP tools. To minimize and manage these risks, Google Cloud offers defaults 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.

The Memorystore for Valkey MCP server doesn't have its own quotas. There is no limit on the number of call that can be made to the MCP server. You are still subject to the quotas enforced by the APIs called by the MCP server tools.

• Explore the Memorystore for Valkey remote MCP server reference documentation, which includes a list of all available tools, and the full input and output schema for each tool.
• See the Memorystore for Valkey overview.
• Learn about MCP security and governance.