Model Context Protocol

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Jlowin

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

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Grafana

MCP server for Grafana

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Punkpeye

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

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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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Secretiveshell

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

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LastmileAi

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

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Manusa·cloud

Model Context Protocol (MCP) server for Kubernetes and OpenShift

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Rectalogic·cloud

Model Context Protocol tool support for LangChain

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Cyberchitta·cloud

Share code with LLMs via Model Context Protocol or clipboard. Profile-based customization enables easy switching between different tasks (like code review and documentation). Code outlining support is available as an experimental feature.

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LastmileAi·cloud

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

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Kimtth·cloud

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

Sooperset logo
Sooperset·cloud

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

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.