Model Context Protocol

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Jlowin

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

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Grafana

MCP server for Grafana

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Sooperset

Model Context Protocol (MCP) server for Atlassian products (Confluence and Jira). This integration supports both Atlassian Cloud and Jira Server/Data Center deployments.

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Punkpeye

A TypeScript SSE proxy for MCP servers that use stdio transport.

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Secretiveshell

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

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Ferrislucas

A Model Context Protocol server that executes commands in the current iTerm session - useful for REPL and CLI assistance

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Andybrandt logo
AndybrandtΒ·cloud

An MCP server that provides access to arXiv papers through their API.

Andybrandt logo
AndybrandtΒ·cloud

MCP server that gives Claude ability to use OpenAI's GPTs assistants

Adhikasp logo
AdhikaspΒ·cloud

A Model Context Protocol (MCP) server for interacting with Twitter.

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IsaacwassermanΒ·cloud

A Model Context Protocol (MCP) server implementation that provides the LLM an interface for visualizing data using Vega-Lite syntax.

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MzxraiΒ·cloud

MCP web research server (give Claude real-time info from the web)

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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.