LLM Function Calling & Tool Use
Implements production-ready tool calling patterns and structured output schemas for AI agents.
The Function Calling skill provides a comprehensive framework for enabling LLMs to interact with external tools and return guaranteed structured data. It covers the entire tool integration lifecycle, from defining precise JSON schemas with 2026 'strict mode' best practices to implementing resilient asynchronous execution loops. By providing clear patterns for parallel tool calls, Pydantic-based validation, and error handling, this skill allows developers to build reliable autonomous agents and data extraction pipelines that maintain high consistency and type safety.
주요 기능
01Type-safe structured output generation using Pydantic and Zod
02Concurrent parallel tool call management for reduced latency
03Strict-mode tool definitions for guaranteed schema compliance
0469 GitHub stars
05Asynchronous tool execution loops for multi-step agent reasoning
06Standardized error handling and parameter validation patterns
사용 사례
01Implementing reliable tool-use loops in LangChain or custom agent frameworks
02Building autonomous agents that interact with external APIs and databases
03Extracting complex structured data from unstructured text for system ingestion