You want to build an AWS Step Functions workflow, and you have an AI coding agent open in your terminal or IDE. But the agent doesn’t know about Amazon States Language (ASL), service integrations, or how to deploy state machines. Before you can start, you need to find the right Model Context Protocol (MCP) server package, figure out the configuration format for your specific agent, and set up credentials. AWS Step Functions has added a “Copy agent prompt” button to the AWS Step Functions console that removes this setup entirely.
You choose the button, paste the prompt into your agent, and the agent configures itself with Serverless skills and an MCP server. You can start building workflows with natural language immediately. The feature works with Claude Code , Kiro CLI , Cursor, GitHub Copilot, Codex, Devin Desktop, OpenCode, and any other MCP-compatible agent. The button appears in three places in the Step Functions console: Figure 1: Step Functions console modal showing the Copy agent prompt The copied prompt is a fetch instruction that points to a setup guide hosted on AWS documentation.
You paste it into your agent, and the agent installs two things: AWS Serverless skill (from the Agent Toolkit for AWS ) provides your agent with deep context on Step Functions. It includes how to write ASL, structure workflows with retries and error handling, choose between Standard and Express workflow types, implement patterns like saga orchestration and parallel fan-out, and deploy using AWS Serverless Application Model (AWS SAM) or AWS Cloud Development Kit (AWS CDK). AWS Serverless MCP Server gives your agent direct access to AWS.
Through the Model Context Protocol, your agent can create and update state machines, start and describe executions, inspect workflow history, and manage resources in your account. The setup guide auto-detects your agent and provides the correct configuration format: If you use a different MCP-compatible agent, the guide provides a generic JSON configuration block you can add to your agent’s config file. Once your agent is configured, you can describe workflows in natural language, and the agent produces valid, deployable state machines.