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Why this server?
This server enables Claude and other LLMs to interact with Notion workspaces, providing capabilities like searching, retrieving, creating and updating pages, as well as managing databases, which are all forms of workspace control.
Why this server?
A Dart-based MCP server implementation that enables AI-assisted task management, document handling, and workspace organization through standardized tools and seamless Dart integration.
Why this server?
Leverage SettleMint's Model Context Protocol server to seamlessly interact with enterprise blockchain infrastructure. Build, deploy, and manage smart contracts through AI-powered assistants, streamlining your blockchain development workflow for maximum efficiency.
Why this server?
Swiss MCP is your AI-powered command center for orchestrating complex tasks with ease. Think of it as your personal AI assistant that can coordinate multiple AI tools to accomplish amazing things!
Why this server?
Enables seamless integration between Home Assistant and Language Learning Models (LLMs), allowing natural language interaction for smart home control and automation management.
Why this server?
Lets you use Claude Desktop to interact with your task management data in Things app, enabling you to create tasks, analyze projects, manage priorities, and implement productivity workflows through natural language.
Why this server?
A Model Context Protocol server that connects Claude and other AI assistants to your Notion workspace, allowing AIs to interact with databases, pages, and blocks.
Why this server?
Enables enterprise-grade authentication management with secure credential handling and support for multi-protocol auth, complete with tools for analyzing, setting up, and testing authentication systems.
Why this server?
A server that enables LLMs to programmatically interact with Logseq knowledge graphs, allowing creation and management of pages and blocks.
Why this server?
Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references.