Empower your agents with the Google Cloud CLI remote MCP server

Google Cloud launched the Google Cloud CLI remote MCP server in preview, packaging hundreds of gcloud and bq commands into a single managed MCP server that gives AI agents access to Google Cloud infrastructure operations. The server runs commands in an isolated, network-restricted sandbox with no ambient credentials, enforcing authentication through Agent Identity, OAuth 2.0, and IAM, and integrates Model Armor to screen prompts and responses against prompt injection. It can log every tool invocation to Audit Logs under cloudcli.googleapis.com/mcp, giving security teams visibility into caller identities and IAM authorization decisions without exposing command payloads or PII.

Today, we’re expanding our ecosystem of managed remote MCP servers by introducing the Google Cloud CLI remote MCP server https://docs.cloud.google.com/sdk/use-gcloud-mcp in preview. Powered by the popular gcloud https://docs.cloud.google.com/sdk/gcloud and bq BigQuery https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference command-line tools, this new server gives AI agents immediate, broad access to command-line operations for managing Google Cloud infrastructure and working with advanced BigQuery workflows securely and seamlessly. Agents are increasingly performing complex cloud operations, but standardizing how they interact with backend systems remains a challenge. The Google Cloud CLI remote MCP server bridges this gap by packaging the versatility of hundreds of gcloud and bq commands into one single MCP server. This results in two strong benefits for the agent: Higher-level abstractions: CLI commands package complex multi-step workflows, validation checks, and high-level operations into unified commands rather than requiring multi-step API orchestration. Leverages model training: LLMs are heavily pre-trained on public command-line documentation, syntaxes, and usage examples, making CLI invocation intuitive and highly accurate for models. Managing cloud infrastructure with AI agents traditionally requires installing and maintaining Google Cloud CLI binaries inside agent execution environments. The Cloud CLI remote MCP server bridges CLI capabilities with MCP benefits by providing an isolated execution sandbox on Google Cloud infrastructure. This solves key infrastructure challenges: Simplified dependency and runtime management: For teams building custom agents, maintaining local CLI versions and dependencies across dev, test, and production environments creates operational overhead. Remote MCP eliminates local installations and runtime maintenance. Access for web-based agent endpoints: Web-hosted agent platforms and web interfaces such as Gemini Enterprise and other hosted enterprise agent platforms run in environments where users cannot control or install local packages. Remote MCP enables secure, managed access to Google Cloud CLI operations directly from these surfaces. Connecting an AI agent to your infrastructure requires strict, enterprise-ready safeguards. This remote server leverages Google Cloud's standard identity and governance frameworks to keep your environments secure: Zero ambient credentials: The server isolates execution in a network-restricted proxy boundary with no ambient credentials. Authentication and authorization are handled through Agent Identity https://docs.cloud.google.com/iam/docs/agent-identity-overview , OAuth 2.0 https://developers.google.com/identity/protocols/oauth2 , and Identity and Access Management IAM https://docs.cloud.google.com/iam/docs . Strict policy enforcement: Every command executed through the remote MCP server is run with the permissions of the authenticated caller identity. Both standard IAM permissions and organization policy service constraints are strictly enforced against downstream target resources. Advanced protection with Model Armor: To minimize the risks associated with AI tool calling, the Cloud CLI remote MCP server integrates with Model Armor https://docs.cloud.google.com/model-armor/model-armor-mcp-google-cloud-integration . You can proactively screen LLM prompts and responses to protect against risks like prompt injection and malicious inputs. Cloud audit logging: The Cloud CLI remote MCP server can be configured to log every tool invocation to Audit Logs Data Access logs under cloudcli.googleapis.com/mcp . Security teams can gain full visibility into caller identities, OAuth clients, and IAM authorization decisions mcp.googleapis.com/tools.call without exposing sensitive command payloads or personally identifiable information PII . Integrating cloud management into your agents no longer requires packaging Google Cloud CLI binaries, managing local execution runtimes, or maintaining dependencies inside agent container images. Because the Google Cloud CLI remote MCP server implements the standard Model Context Protocol, any MCP-compatible agent platform or orchestration runtime can connect immediately via standard configuration: Your agent immediately gains access to execute gcloud and bq commands in a secure, network-isolated cloud sandbox. Authentication is handled via keyless Agent Identity for hosted Google Cloud platforms, or standard OAuth 2.0 for external runtimes. For authentication options, see the MCP Authentication Guide https://docs.cloud.google.com/mcp/set-up-authentication-mcp-servers . The Cloud CLI remote MCP server https://docs.cloud.google.com/sdk/use-gcloud-mcp exposes two powerful tools, run gcloud command and run bq command , giving your AI agents broad, immediate access to Google Cloud operations through natural language. Managing cloud infrastructure with run gcloud command With run gcloud command , agents can execute the full breadth of gcloud operations to manage, diagnose, and secure your Google Cloud environment. An example follows: Observability and incident diagnostics : An agent streamlines incident diagnostics by automating command execution and reducing context-switching across tools. While the BigQuery MCP server https://docs.cloud.google.com/bigquery/docs/use-bigquery-mcp already helps organizations analyze and explore data using AI agents, with the introduction of run bq command , agents can now tackle advanced BigQuery tasks such as resource allocation, job monitoring, and task scheduling by unlocking the full scope of bq CLI https://docs.cloud.google.com/sdk/reference/mcp mcp-tools functionality. Key capabilities include: Automating scheduled queries : An agent utilizes BigQuery Data Transfer Service configurations to schedule queries automatically. Job and resource management : Gain deep insight into query execution details, including processed data volume, slot usage, and execution plans, as well as managing reservations. Access and permissions control: Data administrators and owners can inspect and update table permissions directly through the agent. The Google Cloud CLI MCP server is available today in public preview. There is no additional charge to use the MCP server itself. You pay only for the GCP resources you create and any applicable data transfer costs. To get started https://docs.cloud.google.com/sdk/use-gcloud-mcp , enable the Cloud CLI Execution API cloudcli.googleapis.com in your Google Cloud project, grant the required MCP Tool User roles/mcp.toolUser IAM role to your agent or user identity, and configure your MCP client to connect to cloudcli.googleapis.com/mcp .