Model Context Protocol
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A Model Context Protocol server for Excel file manipulation. This server enables workbook creation, data manipulation, formatting, and advanced Excel features.
mcp-golang
23
Write Model Context Protocol servers in few lines of go code. Docs at https://mcpgolang.com
Go server implementing Model Context Protocol (MCP) for filesystem operations.
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A mongo db server for the model context protocol (MCP)
A Model Context Protocol (MCP) server that provides web search functionality using Perplexity AI's API. Works with the Anthropic Claude desktop client.
A TypeScript implementation of a Model Context Protocol (MCP) server that integrates with PiAPI's API. PiAPI makes user able to generate media content with Midjourney/Flux/Kling/LumaLabs/Udio/Chrip/Trellis directly from Claude or any other MCP-compatible apps.
A Model Context Protocol (MCP) server for Rember. Allow Claude to create flashcards for you with the official Model Context Protocol (MCP) for Rember. Rember helps you study and remember anything you care about by scheduling spaced repetition reviews.
Java SDK for the Model Context Protocol (MCP), providing seamless integration between Java and Spring applications and MCP-compliant AI models and tools.
Supabase MCP Server enabling Cursor & Windsurf to use any method from Management API and query your database
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.