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:
- Elicitation (request user input mid-execution)
- Sampling (request LLM-generated content from the client)
- Progress notification (stream real-time updates)
These capabilities transform one-way tool execution into bidirectional conversations between your MCP server and clients.
Model Context Protocol (MCP) is an open standard defining how LLM…