Today, we’re expanding our ecosystem of managed remote MCP servers by introducing the Google Cloud CLI remote MCP server in preview.
Powered by the popular gcloud and bq (BigQuery) 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.
Why CLI matters for AI agents
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:
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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.
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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.
The benefits of putting CLI behind remote MCP
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:
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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.
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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.
Enterprise-grade security and governance
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:
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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, OAuth 2.0, and Identity and Access Management (IAM).
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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.
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Advanced protection with Model Armor: To minimize the risks associated with AI tool calling, the Cloud CLI remote MCP server integrates with Model Armor. You can proactively screen LLM prompts and responses to protect against risks like prompt injection and malicious inputs.
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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).
Connecting to the Google Cloud CLI Remote MCP Server
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:
