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Why this server?
Provides a Model Context Protocol implementation that enables AI-powered access to documentation resources, featuring URI-based navigation, template matching, and structured documentation management.
Why this server?
Enables querying and retrieving content from Confluence through CQL searches and page content fetching, allowing Claude to seamlessly access information stored in Confluence workspaces.
Why this server?
Provides tools for listing and retrieving content from different knowledge bases using semantic search capabilities.
Why this server?
This project implements a Model Context Protocol (MCP) server for connecting AI models with Obsidian knowledge bases. Through this server, AI models can directly access and manipulate Obsidian notes, including reading, creating, updating, and deleting notes, as well as managing folder structures.
Why this server?
A Model Context Protocol server that provides Claude and other LLMs with read-only access to Hugging Face Hub APIs, enabling interaction with models, datasets, spaces, papers, and collections through natural language.
Why this server?
Facilitates searching and accessing programming resources across platforms like Stack Overflow, MDN, GitHub, npm, and PyPI, aiding LLMs in finding code examples and documentation.
Why this server?
A server that allows AI assistants to browse and read files from specified GitHub repositories, providing access to repository contents via the Model Context Protocol.
Why this server?
A framework that enables websites to share tools, resources, and prompts with client-side LLMs without requiring API keys, allowing users to interact with web services using their preferred models.
Why this server?
Memory Bank Server provides a set of tools and resources for AI assistants to interact with Memory Banks. Memory Banks are structured repositories of information that help maintain context and track progress across multiple sessions.