Build and Deploy a Remote MCP Server to GKE in 30 Minutes

Integrating context from tools and data sources into LLMs remains a friction point for AI agents. Anthropic introduced the Model Context Protocol (MCP) to standardize how applications provide context, but early implementations relied on local stdio transport. The article identifies the need for remote, centralized MCP servers that teams can share, and positions Google Kubernetes Engine (GKE) as a scalable, secure foundation for that deployment.

The guide walks through building a simple math MCP server (add and subtract tools) using FastMCP in Python with the Streamable HTTP transport. It then covers containerizing the server via Docker, pushing to Artifact Registry, and deploying to a GKE Autopilot cluster. The critical operational move is exposing the server through the Kubernetes Gateway API with a Google-managed SSL certificate, ensuring encrypted traffic and a stable endpoint like https://mcp.yourdomain.com/mcp.

For a serious builder, this pattern demonstrates a production‑grade path for deploying remote MCP infrastructure. GKE Autopilot handles horizontal scaling for stateless MCP workloads, while the Gateway API with TLS provides centralized access and security. Teams can update a single server and immediately benefit all consumers, avoiding the overhead of managing redundant local servers. The 30‑minute timeline is realistic given the provided steps and prerequisites.