What is the difference between @agent.tool and @agent.tool_plain?

@agent.tool requires the first parameter to be RunContext for dependency access, while @agent.tool_plain is for simpler functions that do not require access to the agent's context or state.

How do ToolSets help in agent development?

Toolsets allow you to group related functions into modular units, making it easier to manage and compose different capabilities across multiple agents.

Does this skill support asynchronous tool execution?

Yes, it supports both sync and async tools, though async is preferred for I/O-bound tasks to maintain agent performance.

Why are docstrings mandatory for PydanticAI tools?

Docstrings provide the semantic description the LLM needs to understand the tool's purpose and how to populate its parameters during a function call.

Can PydanticAI tools return complex data types?

Yes, tools can return strings, Pydantic models, dictionaries, or specialized ToolReturn objects containing complex media like image URLs.

PydanticAI Tool System

Name: PydanticAI Tool System
Author: existential-birds

byexistential-birds

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数据科学与机器学习

Simplifies the registration and implementation of PydanticAI tools with automated context handling and type-safe function calling.

关于

The PydanticAI Tool System skill provides a standardized framework for building robust AI agent capabilities using the PydanticAI library. It streamlines the process of registering tools with or without execution context, enforces strict type annotations, and ensures docstrings are optimized for LLM comprehension. This skill is particularly useful for developers building complex agentic workflows that require dependency injection, dynamic tool filtering, or modular tool composition through toolsets.

主要功能

14 GitHub stars
Optimized docstring formatting for enhanced LLM tool-calling accuracy
Automated registration for contextual and plain agent tools
Dynamic tool filtering and modification per-request via Tool Prepare
Type-safe dependency injection via RunContext handling
Advanced tool composition using modular FunctionToolsets

使用场景

Implementing role-based tool access control within agentic applications
Standardizing function-calling patterns across large Python-based AI projects
Building sophisticated AI agents with multi-tool capabilities and shared dependencies

关于

主要功能

14 GitHub stars
Optimized docstring formatting for enhanced LLM tool-calling accuracy
Automated registration for contextual and plain agent tools
Dynamic tool filtering and modification per-request via Tool Prepare
Type-safe dependency injection via RunContext handling
Advanced tool composition using modular FunctionToolsets

使用场景

Implementing role-based tool access control within agentic applications
Standardizing function-calling patterns across large Python-based AI projects
Building sophisticated AI agents with multi-tool capabilities and shared dependencies