Course

Tool Use with LLMs in Python

Once you can prompt an LLM, the next challenge is making those interactions reliable enough to use in real systems. You need outputs you can validate and work with programmatically, ways to extend what the model can do through tools, and patterns that handle the inevitable edge cases. This course teaches you the core patterns that turn experimental prompts into dependable building blocks—from structured outputs with automatic repair to multi-step tool execution to reusable tool servers.

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4.8 (359 reviews)
29 learners enrolled in this course.
  • Intermediate friendly
  • 6 hours
  • Self paced
  • 3 lessons
Otavio

“The learning paths on Dataquest are incredible. They give you a direction through the learning process – you don’t have to guess what to learn next.”

Otávio Silveira

Data Analyst @ Hortifruti

Tool Use with LLMs in Python

Enroll For Free

Course overview

Move from experimental prompts to reliable LLM systems. This course teaches you the engineering patterns that make LLM interactions dependable: structured outputs with validation, function calling for tool integration, and the Model Context Protocol for reusable tool servers. You’ll learn how to handle messy LLM outputs, build agentic loops that execute multi-step tasks, and create maintainable components that work consistently.

Key skills

  • Creating LLM workflows with consistent, validated JSON outputs
  • Implementing function calling patterns for tool-augmented models
  • Building MCP servers that expose tools to any compatible client
  • Designing multi-step workflows with proper error handling
  • Testing and versioning prompts as maintainable infrastructure

Course outline

Tool Use with LLMs in Python [3 lessons]

Advanced Prompting Patterns 2h

Lesson Objectives
  • Define and validate LLM outputs using Pydantic schemas
  • Build automatic repair loops for invalid JSON responses
  • Organize prompts into reusable, maintainable template blocks
  • Create multi-step pipelines for complex generation tasks
  • Version and test prompts for production reliability

Function Calling 2h

Lesson Objectives
  • Understand function calling: models request, code executes
  • Implement agentic loops for multi-step tool execution
  • Define tools with clear names, descriptions, and schemas
  • Handle errors gracefully by returning structured error messages
  • Execute tools safely with validation and exception handling

Tool Integration with MCP 2h

Lesson Objectives
  • Convert function calling tools into MCP server implementations
  • Bridge MCP servers and OpenAI API using helper functions
  • Integrate MCP tools into existing agentic loop architectures
  • Extract tool definitions and executables from FastMCP servers
  • Evaluate tradeoffs between direct function calling and MCP

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Aaron Melton

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