Google Cloud published a guide for building and deploying a remote MCP server on GKE, targeting secure context delivery for AI agents.
Why a remote MCP server on Kubernetes
AI agents need more than a chat model. They need tools, documents, and live system context delivered in a controlled way. The Model Context Protocol (MCP) defines how a host application talks to a server that exposes those capabilities. Running that server remotely—rather than as a local process on a developer laptop—lets teams share one well-governed endpoint across agents, environments, and teams.
Google Cloud’s guide walks through building and deploying that remote MCP server on GKE. The goal is secure context delivery: agents get the tools and data they are allowed to use, without embedding credentials or private knowledge inside every client. Kubernetes is a natural fit because you already get isolation, networking policy, secrets handling, and a path to scale or replace instances without rewriting agent code.
What you are actually deploying
A remote MCP server is an HTTP-reachable service that implements the protocol: list tools and resources, accept tool calls, and return structured results. On GKE, that usually means a container image, a Deployment (or similar workload), a Service for cluster-internal traffic, and often an Ingress or gateway for external access with TLS. Configuration that is environment-specific—API keys for backend systems, database endpoints, feature flags—belongs in ConfigMaps and Secrets, not baked into the image.
The guide’s value is less about inventing a new protocol and more about showing a production-shaped path: how the server process runs in a pod, how it authenticates callers, and how it reaches only the backends it needs. That separation matters. The agent host decides when to call the server; the server decides which tools exist and how they are executed under your security rules.
Security is the product, not a side note
Remote MCP exists because local tool bridges are hard to audit at scale. Once the server is network-accessible, you must treat it like any other API that can act on behalf of users or systems. Practical controls include mutual TLS or strong identity at the edge, short-lived tokens scoped to the caller, network policies that limit egress to approved services, and least-privilege service accounts for the workload.
- Authenticate every request; never assume cluster-internal traffic is automatically trusted.
- Scope tools narrowly so a compromised agent cannot invoke administrative actions by default.
- Log tool invocations with caller identity, tool name, and outcome for later review.
- Keep secrets out of logs and out of model-visible error messages.
Context delivery is secure only if both paths are controlled: what agents may request, and what the server may do when it fulfills that request. GKE gives you the building blocks; the application design still has to use them deliberately.
How teams should use the guide
Start with a small set of high-value tools—read-only lookups, ticket status, or internal docs search—before exposing write actions. Deploy to a non-production cluster first, verify tool listing and call semantics from a real agent host, then harden identity and network rules. Document for callers which tools exist, what inputs they accept, and what failure modes look like, so agent builders do not guess.
Treat the remote MCP server as shared platform infrastructure: version the image carefully, roll out with health checks, and plan for multi-environment promotion. When Google Cloud documents this pattern on GKE, it is pointing at a durable shape for agent backends: protocol at the edge, Kubernetes for operations, and security designed in rather than patched on after the first successful demo.