Model Context Protocol
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MCP-Bridge
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A middleware to provide an openAI compatible endpoint that can call MCP tools
mcp-golang
13
Write Model Context Protocol servers in few lines of go code. Docs at https://mcpgolang.com
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CodeMirror extension to hook up a Model Context Provider (MCP)
A docker MCP Server (modelcontextprotocol) for seamless container and compose stack management through Claude AI.
A TypeScript framework for building MCP servers.
The fast, Pythonic way to build Model Context Protocol servers 🚀
Model Context Protocol (MCP) server to interact with Firebase services.
An implementation of Anthropic's Model Context Protocol for the Apache Unomi CDP
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.
A Model Context Protocol server for converting almost anything to Markdown
A simple, composable framework to build agents using Model Context Protocol and simple workflow patterns
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.