Designing AI Agents with the Model Context Protocol: From Answers to Actions
summary
Shows a concrete architecture: an app hosts the agent loop that sends user queries plus a fetched tool list to an LLM, parses its tool-invocation responses, calls a local or remote MCP server to run functions, and feeds results back for final natural-language output. Tool design rules include simple functions with explicit docstrings, input validation and guardrails, and no destructive ops; safety uses human-in-the-loop patterns—explain before you act and confirm before you act—and automated evals integrated into CI/CD.
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chapters (13)
- 0:00Introduction
- 1:26From text to action
- 5:01What defines an AI agent
- 7:09Tools as eyes and hands
- 9:17Agent loop and orchestration
- 12:25Model Context Protocol
- 16:24MCP architecture
- 18:31LLM, MCP server and app interaction
- 21:41Safety in agentic systems
- 24:10Evaluating agents with Evals
- 26:37Live MCP agent demo
- 29:29Trustworthy AI workflows
- 30:12Closing
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