Model Context Protocol
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Go server implementing Model Context Protocol (MCP) for filesystem operations.
A Model Context Protocol (MCP) server implementation that provides Elasticsearch interaction. This server enables searching documents, analyzing indices, and managing cluster through a set of tools.
Write Model Context Protocol servers in few lines of go code. Docs at https://mcpgolang.com
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A TypeScript-based MCP server that provides tools to interact with Confluence. It demonstrates core MCP concepts.
A Model Context Protocol (MCP) server implementation that provides Elasticsearch interaction. This server enables searching documents, analyzing indices, and managing cluster through a set of tools.
Foxy contexts is a Golang library for building context servers supporting Model Context Protocol.
A Google Tasks Model Context Protocol Server for Claude
A Model Context Protocol server that provides access to MongoDB databases. This server enables LLMs to inspect collection schemas and execute MongoDB operations.
Model Context Protocol (MCP) Server for Apify's Actors
A TypeScript framework for building MCP servers.
The fast, Pythonic way to build Model Context Protocol servers 🚀
Go server implementing Model Context Protocol (MCP) for filesystem operations.
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.