Deployment & Runtime

These packages give you a FaaS runtime where functions are exposed as MCP tools, letting AI agents call them directly without cold starts. You'd adopt them to eliminate the latency of cold-start function invocations while making your serverless functions natively callable by AI agents through the Model Context Protocol. The most distinctive thing is that functions are MCP tools first and HTTP endpoints second, with a warm pod pool manager that keeps pre-warmed containers ready for sub-100ms invocations.

These Terraform modules deploy a Model Context Protocol (MCP) server workload onto Amazon EKS, provisioning the full supporting infrastructure: a cluster, ElastiCache Redis for session state, SQS task and result queues with a dead-letter queue, Secrets Manager secrets, and a CloudWatch dashboard with SNS alerts. You would adopt them to get a production-ready MCP deployment on AWS without wiring together EKS, KEDA, Redis, SQS, IRSA, and monitoring yourself. The modules are designed as composable building blocks—the root module wires everything together for a drop-in experience, while individual submodules let you swap in your own KEDA installation or selectively assemble only the pieces you need.

These Terraform modules deploy any MCP (Model Context Protocol) server onto Google Cloud Run, wiring it to Firestore for session state, Secret Manager for credentials, and Pub/Sub for async task distribution. You'd use them to get a production-ready MCP server infrastructure on GCP without designing the networking, IAM, observability, and persistence plumbing yourself. The modules are designed as drop-in components that compose through Terraform outputs and service account emails, letting you mix and match Cloud Run, Firestore, Secret Manager, and Pub/Sub modules independently for different environments or service topologies.