Model Context Protocol

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Jlowin

The fast, Pythonic way to build Model Context Protocol servers 🚀

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Secretiveshell

A middleware to provide an openAI compatible endpoint that can call MCP tools

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Punkpeye

A TypeScript framework for building MCP servers.

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LastmileAi

A simple, composable framework to build agents using Model Context Protocol and simple workflow patterns

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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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Kimtth logo
Kimtth·cloud

A minimal Model Context Protocol 🖥️ server/client🧑‍💻with Azure OpenAI and 🌐 web browser control via Playwright.

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Ergut·cloud

A Model Context Protocol (MCP) server that provides secure, read-only access to BigQuery datasets. Enables Large Language Models (LLMs) to safely query and analyze data through a standardized interface.

Wong2 logo
Wong2·cloud

A CLI inspector for the Model Context Protocol

Quantgeekdev logo
Quantgeekdev·cloud

A framework for building Model Context Protocol (MCP) servers elegantly in TypeScript.

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Adhikasp·cloud

A Model Context Protocol (MCP) server that helps read GitHub repository structure and important files.

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Mark3labs·cloud

A Go implementation of the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources and tools.

MetoroIo logo
MetoroIo·cloud

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

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.