How do I handle multiple tool calls simultaneously?

This skill provides patterns for parallel tool calling using asynchronous execution loops, allowing the LLM to trigger several operations concurrently to reduce latency.

What is the 2026 strict mode for function calling?

Strict mode is a best practice that enforces rigorous JSON schema validation, requiring all properties to be listed as required and disabling additional properties to ensure 100% reliability in LLM responses.

How many tools should I provide to an LLM at once?

For optimal performance and to avoid model confusion, it is recommended to provide between 5 and 15 tools per interaction, depending on the complexity of the task.

Can I use Pydantic with this skill?

Yes, the skill includes specific implementation patterns for using Pydantic models to define tool parameters and automatically parse structured LLM responses into typed Python objects.

LLM Function Calling & Tool Use

Name: LLM Function Calling & Tool Use
Author: yonatangross

byyonatangross

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Data Science & ML

Implements production-ready tool-use patterns and structured output schemas for LLMs using the latest industry standards.

About

This skill provides a comprehensive framework for enabling Large Language Models to interact with external tools and return guaranteed structured data. It focuses on the latest standards, including strict schema validation, robust tool execution loops, and Pydantic-based structured outputs. Whether you are building autonomous agents or complex API integrations, this skill ensures your LLM interactions are reliable, type-safe, and follow modern best practices like parallel tool call management and strict JSON enforcement.

Key Features

Asynchronous tool execution loops with multi-step reasoning
Type-safe structured output generation using Pydantic
Strict-mode tool definitions for guaranteed schema compliance
Parallel tool call implementation and optimization
LangChain tool binding and model integration patterns
29 GitHub stars

Use Cases

Building autonomous AI agents that perform database searches and calculations
Integrating LLMs with external APIs via robust, validated tool-calling interfaces
Extracting structured data from unstructured text with 100% JSON reliability

About

Key Features

Asynchronous tool execution loops with multi-step reasoning
Type-safe structured output generation using Pydantic
Strict-mode tool definitions for guaranteed schema compliance
Parallel tool call implementation and optimization
LangChain tool binding and model integration patterns
29 GitHub stars

Use Cases

Building autonomous AI agents that perform database searches and calculations
Integrating LLMs with external APIs via robust, validated tool-calling interfaces
Extracting structured data from unstructured text with 100% JSON reliability