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pydantic-ai-tool-system

Register PydanticAI tools with decorators, context, and docstrings.

Updated Feb 17, 2026
Search Tags:#pydantic#python#agent development#ai tools#function calling#tool registration
One-click install
npx skills add https://github.com/javierhbr/random-poc --skill pydantic-ai-tool-system-javierhbr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydantic-ai-tool-system
Source: https://github.com/javierhbr/random-poc/tree/main/custom-skills/beagle-main/plugins/beagle-ai/skills/pydantic-ai-tool-system
Command: npx skills add https://github.com/javierhbr/random-poc --skill pydantic-ai-tool-system-javierhbr

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill simplifies the process of defining, registering, and managing tools for AI agents, ensuring proper context handling, type annotations, and documentation for seamless function calling and agent actions.

Core Features & Use Cases

  • Tool Definition: Use PydanticAI decorators (@agent.tool, @agent.tool_plain) to define tools.
  • Context Management: Handle agent context (RunContext) and dependencies gracefully.
  • Docstring Integration: Ensure tools are well-documented for LLM understanding using Google or Sphinx styles.
  • Use Case: When building an agent that needs to interact with a database and an external API, use this Skill to define and register the specific functions for querying users and fetching data, ensuring the agent can call them correctly with the right parameters and context.

Quick Start

Use the pydantic-ai-tool-system skill to register a new tool named 'get_user_data' that takes a user ID and returns user information.

Frequently Asked Questions about pydantic-ai-tool-system

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I define and register AI tools in PydanticAI for function calling?▼

Tool registration requires using `@agent.tool` or `@agent.tool_plain` decorators with precise type annotations and Google or Sphinx docstrings, yielding LLM-interpretable agent capabilities for function calling.

What's the best way to manage RunContext and dependencies for PydanticAI agent actions?▼

Manage RunContext and dependencies by passing them through decorated tool functions, producing agent actions that maintain proper context handling throughout the function calling process.

Do I need specific docstring formats for PydanticAI tools to ensure LLM interpretability?▼

Adhere to Google or Sphinx docstring formats when documenting tools, generating structured documentation that directly ensures LLM interpretability of function parameters and actions.

Can I use PydanticAI to build agents that interact with databases and external APIs?▼

Implement database queries and external API fetches as registered tools, creating agent capabilities that execute specific function calling mechanisms for data retrieval with proper context.

Why does my PydanticAI agent fail to call registered tools correctly?▼

Agent tool calling fails when decorator rules are ignored, docstrings are missing, or type annotations are incorrect, preventing the LLM from interpreting the function parameters and context required for execution.

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