MCP Servers for Pydantic

Pydantic is a Python library for data validation and settings management using Python type annotations.

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  • Why this server?

    Utilizes Pydantic for robust input validation using Pydantic models to ensure data integrity for time-related operations

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    A Python microservice that provides utilities for retrieving current times and converting between different global timezones with robust validation.
    Last updated -
    Python
    MIT License
  • Why this server?

    Uses Pydantic models to structure and validate data retrieved from Weibo, including user profiles, feeds, and search results.

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    A Model Context Protocol server for scraping Weibo user information, feeds, and search functionality. It helps retrieve detailed user profiles, timeline content, and perform user searches on Weibo.
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    3
    5
    Python
    MIT License
  • 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.

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    An implementation that enables Claude to connect to ServiceNow instances, retrieve data, and perform actions through the ServiceNow API.
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    62
    53
    Python
    MIT License
    • Apple
  • Why this server?

    Used for configuration settings and validation of data structures within the bridge

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    A protocol bridge that connects AI systems to Taiga project management platform, enabling AI tools to create and manage projects, epics, user stories, tasks, issues, and sprints.
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    60
    3
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    MIT License
  • 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.

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    A Model Context Protocol server that retrieves detailed geographic information about IP addresses using the ipinfo.io API, helping determine user location and network details.
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    5
    Python
    MIT License
  • Why this server?

    The MCP server leverages Pydantic for data validation and settings management.

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    A FastMCP server that provides real-time weather data from KNMI weather stations, allowing users to access temperature, humidity, wind speed, and other weather metrics for any location in the Netherlands through natural language queries.
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    4
    1
    Python
    MIT License
    • Apple
  • Why this server?

    Uses Pydantic models for data validation and structure, as indicated in the project structure.

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    An interactive Python code execution tool that allows users and LLMs to safely execute Python code and install packages in isolated Docker containers.
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    6
    Python
    Apache 2.0
  • Why this server?

    Uses Pydantic for configuration management, providing type validation and settings management for the DICOM MCP server.

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    Enables AI assistants to query and analyze medical imaging metadata from DICOM servers, including patient information, studies, series, and instances, as well as extract text from encapsulated PDF documents.
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    33
    Python
    MIT License
  • Why this server?

    Utilizes Pydantic for type-safe request/response handling, ensuring data validation and consistent interactions with the Supabase database.

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    A Model Context Protocol server that enables AI tools to interact with Supabase databases, providing tools for reading, creating, updating, and deleting records in Supabase tables.
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    Python
    MIT License
  • Why this server?

    Uses Pydantic for data validation when processing natural language commands and CAD operation parameters.

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    Enables controlling CAD software (AutoCAD, GstarCAD, ZWCAD) through natural language instructions, allowing users to create and modify drawings without manually operating the CAD interface.
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    53
    Python
    MIT License
  • Why this server?

    Used for data validation and settings management in the MSSQL MCP server implementation

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    Provides database interaction and business intelligence capabilities, enabling users to run SQL queries, analyze business data, and automatically generate business insight memos for Microsoft SQL Server databases.
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    29
    Python
    MIT License
    • Apple
    • Linux
  • Why this server?

    Leverages Pydantic for data validation and parsing of API request/response models defined in OpenAPI specifications.

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    A Model Context Protocol Server that enables LLMs to interact with and execute REST API calls through natural language prompts, supporting GET/PUT/POST/PATCH operations on configured APIs.
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    5
    Python
    Apache 2.0
  • Why this server?

    Utilizes Pydantic for settings management and validation of configuration values.

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    Enables AI models to publish and consume messages from Apache Kafka topics through a standardized interface, making it easy to integrate Kafka messaging with LLM and agent applications.
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    Apache 2.0
    • Linux
    • Apple
  • Why this server?

    Leverages Pydantic for data validation across the service's operations and request handling.

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    An MCP-native server that enables intelligent task delegation from advanced AI agents like Claude to more cost-effective LLMs, optimizing for cost while maintaining output quality.
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    29
    Python
    MIT License
  • 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.

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    A Model Context Protocol server that enables LLMs to retrieve and analyze OpenTelemetry traces and metrics from Logfire, supporting exception tracking and custom SQL queries against telemetry data.
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    Python
  • Why this server?

    Uses Pydantic for structured data models of DraCor entities, ensuring type safety and validation.

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    A Model Context Protocol server that enables Claude and other LLMs to interact with the Drama Corpora Project API for analyzing dramatic texts, character networks, and play information across different languages and periods.
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    2
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    MIT License
    • Linux
    • Apple
  • Why this server?

    Uses Pydantic for type validation of URL parameters and request configuration

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    A Model Context Protocol (MCP) server that enables Claude or other LLMs to fetch content from URLs, supporting HTML, JSON, text, and images with configurable request parameters.
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    Python
    MIT License
  • Why this server?

    The README showcases the use of pydantic_settings.BaseSettings for application configuration in the example code.

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    Eunomia MCP Server is an extension of the Eunomiaframework that connects Eunomia instruments with servers. It provides a simple way to orchestrate data governance policies (like PII detection or user access control) and seamlessly integrate them with external serve
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    5
    Python
    Apache 2.0
  • 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.

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    A Python-based MCP server that integrates the TapTools API, enabling AI models to fetch Cardano blockchain data, including tokens, NFTs, market stats, and wallet info, through standardized tools.
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    Python
    MIT License
  • Why this server?

    Provides type-safe parameter handling for the resource template system, enabling validation and extraction of parameters from URIs

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    A Model Context Protocol implementation that enables AI-powered access to documentation resources, featuring URI-based navigation, template matching, and structured documentation management.
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    3
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    MIT License
  • Why this server?

    Provides data validation and settings management for the API

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    Provides API endpoints for a hotel management frontend and integrates with SmartThings API to control devices based on user preferences and room assignments.
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    Python
  • Why this server?

    Provides type-safety and validation through Pydantic models for robust data handling and configuration.

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    A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.
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    • Linux
    • Apple
  • Why this server?

    Enables robust data validation for thought steps in the sequential thinking process, ensuring input integrity before processing by the agent team.

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    An advanced MCP server that implements sophisticated sequential thinking using a coordinated team of specialized AI agents (Planner, Researcher, Analyzer, Critic, Synthesizer) to deeply analyze problems and provide high-quality, structured reasoning.
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    124
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    • Linux
    • Apple
  • Why this server?

    Provides type validation and safety for all parameters using Pydantic models

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    A Model Context Protocol server implementation that provides a standardized interface for interacting with Spiral's language models, offering tools to generate text from prompts, files, or web URLs.
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    14
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    • Linux
    • Apple
  • Why this server?

    Uses Pydantic models to provide type-safe interfaces for working with data from macOS applications.

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    A Python server that enables interaction with macOS native applications (Contacts, Notes, Mail, Messages, Reminders, Calendar, and Maps) through AppleScript, featuring asynchronous operations and type-safe interfaces.
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    5
    Python
    • Apple
  • Why this server?

    Provides data validation and settings management for the Sequential Questioning server

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    A specialized server that enables LLMs to gather specific information through sequential questioning, implementing the MCP standard for seamless integration with LLM clients.
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    Python
  • Why this server?

    Used for data validation and settings management, structuring agent personalities and ensuring compatibility with the MCP schema format.

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    A bridging framework that integrates Knowledge Organization Infrastructure (KOI) with Model Context Protocol (MCP), enabling autonomous agents to exchange personality traits and expose capabilities as standardized tools.
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  • Why this server?

    Used for data validation and settings management in the price monitoring server

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    Monitors product prices by comparing database reference prices with real-time G-Market prices and sends Slack notifications when prices drop.
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    Python
  • Why this server?

    Utilizes Pydantic for type-safe operations, providing robust type checking and validation throughout the RAG implementation

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    Implements Retrieval-Augmented Generation (RAG) using GroundX and OpenAI, allowing users to ingest documents and perform semantic searches with advanced context handling through Modern Context Processing (MCP).
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    1
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    • Linux
    • Apple
  • Why this server?

    Used for data validation and schema definition in the MCP server's API routes and request handling.

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    A conversational application server that integrates LLM capabilities via Ollama with vector memory context, supporting multiple users, sessions, automatic history summarization, and a plugin system for executing real actions.
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    Python
  • Why this server?

    Handles data validation and serialization for stock information models, ensuring proper formatting of stock data and configuration settings.

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    A FastMCP-based service that provides stock data including historical prices, fundamental information, and financial news.
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    • Linux
    • Apple
  • Why this server?

    Used for automatically generating data models from OpenAPI schemas, providing data validation and serialization for the dynamically loaded plugin definitions.

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    A Python-based MCP server that dynamically fetches plugin definitions from SuperiorAPIs and auto-generates tool functions based on OpenAPI schemas, enabling seamless integration with API services.
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    • Linux
    • Apple
  • Why this server?

    Uses Pydantic for input validation of football data requests and responses

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    Provides programmatic access to comprehensive football statistics and live match data via API-Football, enabling applications to retrieve league standings, team fixtures, player statistics, and real-time match events.
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    Python
  • 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.

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    A server implementation that allows AI models to interact with Jira through the Model Context Protocol, enabling tasks like JQL searches and retrieving issue details.
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    Python
  • Why this server?

    Integrated with FastAPI for robust request/response validation and data modeling, providing built-in validation for the MCP implementation.

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    A production-ready MCP server built with FastAPI, providing an enhanced tool registry for creating, managing, and documenting AI tools for Large Language Models (LLMs).
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    13
    Python