Why use Pydantic for MCP tool definitions?

Pydantic provides a single source of truth for both Python runtime logic and JSON schema generation, ensuring your AI tools always receive valid data that matches the expected structure.

Can I handle complex validation across multiple fields?

Yes, the skill includes implementation patterns for @field_validator and @model_validator to handle cross-field dependency checks and complex business logic.

Does this skill support Pydantic V1?

No, this skill is specifically optimized for Pydantic V2 features like model_json_schema() and the modern ConfigDict syntax for better performance and type safety.

What is 'strict mode' and why is it mandatory here?

Strict mode (ConfigDict(strict=True)) prevents Pydantic from silently converting types—such as turning the string '10' into an integer. This ensures that the LLM is providing the exact data types required by your backend.

MCP Pydantic Tool Definition

Name: MCP Pydantic Tool Definition
Author: Agentient

byAgentient

0•

API Development

Generates robust MCP tool schemas and validation logic using Pydantic V2 to ensure type safety and strict schema compliance.

This skill streamlines the creation of Model Context Protocol (MCP) tool schemas by using Pydantic V2 as the single source of truth. It enforces mandatory strict mode validation to prevent silent type coercion, provides patterns for custom field-level and model-level validation, and automates the generation of JSON schemas for tool registration. By adopting a schema-first development approach, developers can ensure their AI-driven tools receive precisely formatted data, significantly reducing runtime errors and improving the reliability of complex agentic workflows.

Key Features

01Pydantic V2 integration for automated JSON schema generation

02Support for nested models, Union types, and Annotated metadata

03Advanced field and model-level validation patterns for complex logic

04Enforced strict mode validation via ConfigDict to prevent silent data coercion

05Standardized error handling for converting Pydantic errors to MCP responses

060 GitHub stars

Use Cases

01Generating standardized JSON schemas for tool registries and documentation

02Implementing strict data validation for AI-orchestrated tool calls

03Defining type-safe input and output schemas for custom MCP servers

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