Model Context Protocol

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Jlowin

The fast, Pythonic way to build Model Context Protocol servers πŸš€

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Punkpeye

A TypeScript framework for building MCP servers.

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MetoroIo

Write Model Context Protocol servers in few lines of go code. Docs at https://mcpgolang.com

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Strowk

Foxy contexts is a Golang library for building context servers supporting Model Context Protocol.

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Briandconnelly

IP Geolocation Server for MCP

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Zcaceres

A Google Tasks Model Context Protocol Server for Claude

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MetoroIoΒ·cloud

Write Model Context Protocol servers in few lines of go code. Docs at https://mcpgolang.com

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Ktanaka101Β·cloud

A Model Context Protocol (MCP) server implementation for DuckDB, providing database interaction capabilities through MCP tools.

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BriandconnellyΒ·cloud

IP Geolocation Server for MCP

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LamemindΒ·cloud

A middleware server that enables multiple isolated instances of the same MCP servers to coexist independently with unique namespaces and configurations.

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MindscapehqΒ·cloud

MCP Server for Raygun's API V3 endpoints for interacting with your Crash Reporting and Real User Monitoring applications. This server provides comprehensive access to Raygun's API features through the Model Context Protocol.

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Kimtaeyoon83Β·cloud

This is an MCP server that allows you to directly download transcripts of YouTube videos.

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GotoolkitsΒ·cloud

An MCP server application that sends various types of messages to the WeCom group robot.

Understanding Model Context Protocol (MCP)

Model Context Protocol (MCP) is a groundbreaking open-source protocol developed by Anthropic that revolutionizes how AI systems interact with external data sources. As the foundation for next-generation AI interactions, MCP enables AI assistants like Claude to establish secure and standardized connections with various data sources and tools.

Key Features of MCP

  • Universal Standard: Provides a unified framework for AI systems to access external data, tools, and prompts
  • Client-Server Architecture: Implements a robust and scalable architecture for seamless AI interactions
  • Security-First Design: Ensures secure data transmission and access control between AI systems and data sources
  • Tool Integration: Enables AI assistants to interact with various external tools and services
  • Standardized Communication: Establishes consistent protocols for data exchange and interaction patterns

Benefits for AI Development

MCP represents a significant advancement in AI infrastructure, offering developers and organizations a standardized way to build and deploy AI applications. By providing a common protocol, MCP reduces implementation complexity and ensures compatibility across different AI systems and data sources.

Whether you're developing AI applications, managing data infrastructure, or implementing AI solutions, MCP provides the foundation for secure, efficient, and standardized AI interactions. Explore our directory to find MCP servers that match your specific needs and requirements.