Tag: mcp | AI Shipping Labs

AI Data Cleaning Assistant

A production-style app that automates messy CSV cleanup: upload a file, profile it, generate AI-backed cleaning steps, apply deterministic transformations, and download a cleaned dataset with a human-readable report. Turns a repetitive, error-prone task into a repeatable pipeline with tests, an API, and Cloud Run deployment. LLM acts as planner, cleaning engine is deterministic for reliability.

Dec 15, 2025

Event

Build a Course FAQ Agent with MCP, PydanticAI and OpenAI

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: AI Bootcamp: From RAG to Agents Prerequisite workshop: Building a Coding Agent Data Engineering Zoomcamp FAQ source document Parsed FAQ JSON FAQ parsing notebook 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.

Sep 1, 2025

ai-agents llm-engineering agent-systems tooling-architecture mcp rag