Building an Advanced Netflix MCP: Testing the Workflow
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Limitations of Using Raw OpenAI
Throughout this project we have driven the agentic loop ourselves.
Building an MCP client with raw OpenAI APIs works, but it forces you to own every layer of the stack. Tool parsing, message routing, error handling, conversation state — all of it lives in your code as glue. This is fine for learning how MCP works under the hood, but as soon as requirements grow (multiple tools, multi-step reasoning, persistent sessions), the codebase becomes hard to maintain and easy to break.
Practical MCP with FastMCP & LangChain
Engineering the Agentic ExperienceEnroll now to unlock current content and receive all future updates for free. Your purchase supports the author and fuels the creation of more exciting content. Act fast, as the price will rise as the course nears completion!