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Building AI Agents with MCP, PydanticAI and OpenAI

September 1, 2025, 02:00 Europe/Berlin
0 people attended
Alexey Grigorev
ai-agents llm-engineering agent-systems tooling-architecture mcp rag

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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, compare toyaikit, OpenAI Agents SDK, and PydanticAI, and finally move the FAQ tools behind an MCP server that can be used from a notebook, PydanticAI, Cursor, and VS Code.

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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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