Model Context Protocol
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Model Context Protocol (MCP) server for Atlassian products (Confluence and Jira). This integration supports both Atlassian Cloud and Jira Server/Data Center deployments.
MCP-Bridge
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A middleware to provide an openAI compatible endpoint that can call MCP tools
An MCP server implementation that integrates the Brave Search API, providing both web and local search capabilities.
mcp-golang
27
Write Model Context Protocol servers in few lines of go code. Docs at https://mcpgolang.com
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An MCP server that provides LLMs with the latest stable package versions when coding
MCP server for document format conversion using pandoc. This server provides tools to transform content between different document formats while preserving formatting and structure.
Model Context Protocol server to allow for reading and writing from Pinecone. Rudimentary RAG
A TypeScript SSE proxy for MCP servers that use stdio transport.
An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
Model Context Protocol (MCP) CLI server template for Rust
A powerful MCP (Model Context Protocol) Server that audits npm package dependencies for security vulnerabilities. Built with remote npm registry integration for real-time security checks.
Model Context Procotol(MCP) server for using Amazon Bedrock Nova Canvas to generate images
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.