Scaling AI Agent Infrastructure with the MCP Stateless updates- Google Developers Blog

This post details the evolution of the Model Context Protocol (MCP) from a stateful session model to a stateless one, driven by the need for cloud-native scalability. Key changes include the removal of the `initialize` handshake and `Mcp-Session-Id` header, making each request self-describing with metadata in a `_meta` field. This enables standard round-robin load balancing, serverless deployment, transparent failover, and eliminates the need for shared session stores like Redis. HTTP standardization with headers like `Mcp-Protocol-Version` and `Mcp-Method` allows for routing and auditing without deep packet inspection. The introduction of `ttlMs` and `cacheScope` enables intelligent caching. Multi Round-Trip Requests (MRTR) handle elicitation statelessly by returning `InputRequiredResult` with `requestState`. The Tasks Extension allows for asynchronous tool execution via `taskId` without blocking client connections. Security is enhanced with Issuer Verification (RFC 9207) and Resource Indicators (RFC 8707). Tool schemas now fully support JSON Schema 2020-12.