Google Cloud Jumps on the MCP Bandwagon: Managed Support Launches

Deep Dive: MCP and the Cloud

Google Cloud's managed MCP support is a promising development, streamlining integration with their services for AI agents. The unified endpoint and integration with Apigee could significantly reduce development friction. However, the article raises valid concerns about latency compared to local MCP deployments. Edge compute access, which offers lower latency, may be crucial for some applications, and the article correctly points out that remote MCPs could simply be converting the protocol into a remote API. The success of this offering will depend on its performance and whether it can effectively compete with the advantages of local execution, especially for time-sensitive tasks. The public preview is the perfect opportunity to assess this.

Furthermore, while the article highlights industry collaboration through initiatives like Agntcy, the fragmented landscape of AI protocols remains. The reliance on the Agentic AI Foundation and the involvement of multiple hyperscalers suggests a potential battle for dominance in the MCP space. Developers may face vendor lock-in as different cloud providers offer unique MCP implementations and integrations. Careful consideration of vendor lock-in and performance will be crucial when deciding on the best MCP implementation for a given use case.

Key Points

• Google Cloud launches fully managed remote MCP servers, providing a unified layer across Google and Google Cloud services.
• Developers can point AI agents or MCP clients (e.g., Gemini CLI) to a globally consistent endpoint.
• Initial support includes Google Maps, BigQuery, Google Compute Engine (GCE), and Google Kubernetes Engine (GKE).
• Introduces Cloud API Registry and Apigee API Hub to manage and secure MCP capabilities.
• Currently available in public preview.

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📖 Source: Google Cloud Launches Managed MCP Support