Why use Pydantic strict mode for MCP tools?

Strict mode prevents Pydantic from silently coercing types (like converting the string '10' into an integer), ensuring the AI agent sends data exactly as your backend logic expects, reducing unpredictable behavior.

How does this skill handle MCP tool registration?

It utilizes Pydantic's .model_json_schema() method to automatically generate the 'inputSchema' required by the Model Context Protocol, ensuring your documentation and code are always in sync.

Can I use complex types like Unions or nested models?

Yes, the skill provides patterns for advanced schemas including Union types, Literals for enums, nested list models, and annotated fields with specific constraints.

How are validation errors communicated back to Claude?

The skill includes a pattern to catch Pydantic ValidationErrors and format them into structured JSON-RPC error responses that help the AI understand exactly which field failed and why.

MCP Pydantic Tool Architect

Name: MCP Pydantic Tool Architect
Author: Agentient

byAgentient

API 开发

Automates the creation of robust, type-safe Model Context Protocol (MCP) tool schemas using Pydantic V2.

关于

This skill establishes Pydantic V2 models as the single source of truth for Model Context Protocol (MCP) tool definitions. It enforces strict type validation to prevent silent data coercion and automatically generates compliant JSON schemas for seamless tool registration. By providing standardized patterns for complex nested types, custom field validators, and structured error handling, it ensures that AI agents interact with backend services using reliable, well-documented interfaces that minimize runtime failures.

主要功能

Strict Mode Enforcement: Uses ConfigDict(strict=True) to prevent dangerous silent type coercion in tool inputs.
Type-Safe Registration Pattern: Provides boilerplate for registering tools with full IDE support and auto-generated documentation.
Advanced Field Validation: Implements complex constraints, custom field-level validators, and cross-field logic.
Automatic Schema Generation: Derives MCP-compliant JSON schemas directly from Python type hints and Pydantic models.
Structured Error Mapping: Converts Pydantic ValidationErrors into standardized MCP-compliant error responses for the AI.
0 GitHub stars

使用场景

Building production-grade MCP servers with rigorous input validation for AI agents.
Defining complex tool interfaces involving nested models, union types, and literal enums.
Migrating legacy tool definitions to a schema-first, type-safe Python architecture.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add agentient/vibekit mcp-pydantic-tool-definition

For use in Claude.ai and ChatGPT

Download Skill

GitHub

关于

主要功能

Strict Mode Enforcement: Uses ConfigDict(strict=True) to prevent dangerous silent type coercion in tool inputs.
Type-Safe Registration Pattern: Provides boilerplate for registering tools with full IDE support and auto-generated documentation.
Advanced Field Validation: Implements complex constraints, custom field-level validators, and cross-field logic.
Automatic Schema Generation: Derives MCP-compliant JSON schemas directly from Python type hints and Pydantic models.
Structured Error Mapping: Converts Pydantic ValidationErrors into standardized MCP-compliant error responses for the AI.
0 GitHub stars

使用场景

Building production-grade MCP servers with rigorous input validation for AI agents.
Defining complex tool interfaces involving nested models, union types, and literal enums.
Migrating legacy tool definitions to a schema-first, type-safe Python architecture.