Bringing Async MCP to Google Cloud Run — Introducing cloudrun-mcp

Bringing Async MCP to Google Cloud Run — Introducing cloudrun-mcp When you design distributed AI or agentic workloads on Google Cloud’s Cloud Run, you often juggle three recurring problems: How to authenticate workloads securely How to maintain long-lived, event-driven sessions How to stream model context data efficiently without blocking threads cloudrun-mcp solves all three in one lightweight Python SDK. What is MCP (Model Context Protocol)? MCP — Model Context Protocol is an emerging open standard for exchanging context between AI models, tools, and environments. Think of it as “WebSockets for AI knowledge.” Instead of hardcoding API calls, your model connects to an MCP server and streams structured events such as: context.create document.attach agent.reply For developers deploying AI agents on Cloud Run, GKE, or hybrid workloads, an async client is essential for scalability. Introducing cloudrun-mcp Async MCP (Model Context Protocol) client for Cloud Run. Built by Raghava Chellu (F