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
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MCP server to provide Figma layout information to AI coding agents like Cursor
Node.js server implementing Model Context Protocol (MCP) for filesystem operations.
Foxy contexts is a Golang library for building context servers supporting Model Context Protocol.
This plugin provides integration between Genkit and the Model Context Protocol (MCP). MCP is an open standard allowing developers to build "servers" which provide tools, resources, and prompts to clients. Genkit MCP allows Genkit developers to both consume MCP tools, prompts, and resources as a client and provide tools and prompts as a server.
MCP Server for the GitHub API, enabling file operations, repository management, search functionality, and more.
MCP Server for the GitLab API, enabling project management, file operations, and more.
This MCP server integrates with Google Drive to allow listing, reading, and searching over files.
MCP Server for the Google Maps API.
Model Context Protocol (MCP) server implementation providing Google News search capabilities via SerpAPI, with automatic news categorization and multi-language support.
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.