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Build a Course FAQ Agent with MCP, PydanticAI and OpenAI

September 1, 2025 Alexey Grigorev
ai-agents llm-engineering agent-systems tooling-architecture mcp rag

We build a course FAQ assistant from the bottom up. First we expose a plain Python search(query) function to the OpenAI Responses API. Then we turn the same idea into a reusable agent loop and compare toyaikit, OpenAI Agents SDK, and PydanticAI. Finally we move the FAQ tools behind an MCP server. From there a notebook, PydanticAI, Cursor, and VS Code can all reach them.

Links

The main resources:

The system you will build

The final setup looks like this:

flowchart LR NOTEBOOK["Jupyter notebook"] OPENAI["OpenAI Responses API"] FRAMEWORKS["Agents SDK PydanticAI"] MCPCLIENT["MCP clients toyaikit, PydanticAI, Cursor"] MCPSERVER["FastMCP server SSE or stdio"] TOOLS["FAQ tools search, add_entry"] INDEX["minsearch index FAQ JSON"] NOTEBOOK -->|function calling| OPENAI NOTEBOOK --> FRAMEWORKS FRAMEWORKS -->|tool calls| TOOLS NOTEBOOK -->|MCP client| MCPCLIENT MCPCLIENT -->|MCP protocol| MCPSERVER MCPSERVER --> TOOLS TOOLS --> INDEX

The FAQ data comes from the Data Engineering Zoomcamp FAQ. The first half of the workshop keeps the tools inside the notebook so you can see the agent loop directly. The second half moves the same tools into mcp_faq/, which makes them reusable by any MCP client.

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Tutorial pages

  1. Overview and setup
  2. Part 1: Load the FAQ documents
  3. Part 2: Start with a plain model call
  4. Part 3: Make the instructions stronger
  5. Part 4: Replace the loop with ToyAIKit
  6. Part 5: Infer tool schemas from Python functions
  7. Part 6: Install the framework
  8. Part 7: Create the same agent with PydanticAI
  9. Part 8: Move the FAQ tools into a project
  10. Part 9: Connect from Python over stdio
  11. Part 10: Use the FAQ server from Cursor
  12. Q&A
  13. Appendix: file list

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