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In this post, you will learn how to build stateful MCP servers that request user input during execution, invoke LLM sampling for dynamic content generation, and stream progress updates for long-running tasks. You will see code examples for each capability and deploy a working stateful MCP server to Amazon Bedrock AgentCore Runtime.
Stateful MCP client capabilities on Amazon Bedrock AgentCore Runtime now enable interactive, multi-turn agent workflows that were previously impossible with stateless implementations. Developers building AI agents often struggle when their workflows must pause mid-execution to ask users for clarification, request large language model (LLM)-generated content, or provide real-time progress updates during long-running operations, stateless MCP servers can’t handle these scenarios. This solves these limitations by introducing three client capabilities from the MCP specification: