Pydantic is a Python library for data validation and settings management using Python type annotations.
Why this server?
Utilizes Pydantic for robust input validation using Pydantic models to ensure data integrity for time-related operations
Why this server?
Used for configuration settings and validation of data structures within the bridge
Why this server?
Uses Pydantic for parameter validation and data modeling in the ServiceNow MCP server, ensuring type safety when handling ServiceNow API requests and responses.
Why this server?
Uses Pydantic models to structure and validate IP geolocation data, including location, organization, and country details returned from the ipinfo.io API.
Why this server?
The MCP server leverages Pydantic for data validation and settings management.
Why this server?
Used for data validation and settings management in the MSSQL MCP server implementation
Why this server?
Integrates with Logfire, a Pydantic service, to retrieve and analyze application telemetry data through the Logfire APIs using read tokens from the Logfire project settings.
Why this server?
Uses Pydantic for data validation when processing natural language commands and CAD operation parameters.
Why this server?
Uses Pydantic models to structure and validate data retrieved from Weibo, including user profiles, feeds, and search results.
Why this server?
Uses Pydantic for structured data models of DraCor entities, ensuring type safety and validation.
Why this server?
Utilizes Pydantic for settings management and validation of configuration values.
Why this server?
Uses Pydantic for type validation of URL parameters and request configuration
Why this server?
Leverages Pydantic for data validation and parsing of API request/response models defined in OpenAPI specifications.
Why this server?
The README showcases the use of pydantic_settings.BaseSettings for application configuration in the example code.
Why this server?
Uses Pydantic models to structure and validate request data for TapTools API operations, ensuring all JSON requests conform to the expected data schemas.
Why this server?
Provides type-safe parameter handling for the resource template system, enabling validation and extraction of parameters from URIs
Why this server?
Enables robust data validation for thought steps in the sequential thinking process, ensuring input integrity before processing by the agent team.
Why this server?
Uses Pydantic models to provide type-safe interfaces for working with data from macOS applications.
Why this server?
Used for data validation and schema definition in the MCP server's API routes and request handling.
Why this server?
Handles data validation and serialization for stock information models, ensuring proper formatting of stock data and configuration settings.
Why this server?
Uses Pydantic for configuration management, providing type validation and settings management for the DICOM MCP server.
Why this server?
Used for automatically generating data models from OpenAPI schemas, providing data validation and serialization for the dynamically loaded plugin definitions.
Why this server?
Provides type validation and safety for all parameters using Pydantic models
Why this server?
Uses Pydantic for input validation of football data requests and responses
Why this server?
Uses Pydantic for data validation and settings management within the MCP server implementation.
Why this server?
Uses Pydantic for data validation of Jira API requests and responses.
Why this server?
Integrated with FastAPI for robust request/response validation and data modeling, providing built-in validation for the MCP implementation.