collaboration tools를 위한 엄선된 MCP 서버 컬렉션을 찾아보세요. 1397개의 서버를 탐색하고 필요에 맞는 완벽한 MCP를 찾아보세요.
Enables interaction with Google Calendar through Claude Desktop using the Model Context Protocol.
Enables AI assistants to interact with Jira using the Model Context Protocol (MCP).
Connect DevOps platforms via a natural language interface using AI.
Sends notification messages to Microsoft Teams using Azure AD authentication.
Implements a Python server for Feishu (Lark) bots utilizing the Model Context Protocol for standardized messaging.
Connects AI development tools to CodeRide, an AI-native task management system, enabling project understanding, task automation, and collaboration.
Integrates AI assistants with GitLab's merge requests to facilitate automated code review processes.
Integrates FOAAS (Fuck Off As A Service) operations as Model Context Protocol tools for AI clients to generate humorous, explicit responses.
Integrate an AI assistant with GitLab, enabling natural language queries for project data, merge requests, reviews, and pipeline information.
Enables AI agents to efficiently manage GitLab operations with substantial token savings.
Manages work-in-progress to prevent conflicts when multiple Claude Code sessions concurrently modify the same codebase.
Establishes a privacy-first universal memory and profile layer for AI agents, integrating personal and team context across various AI tools.
Enables an AI agent to interact with Bitbucket Cloud repositories, pull requests, and branches.
Integrates leading AI models with live human engineers directly within your terminal.
Enables AI agents to interact with PLANKA kanban boards by providing a Model Context Protocol (MCP) server.
Provides a Model Context Protocol (MCP) server to integrate AI assistants with Jira, Confluence, and Bitbucket.
Provides a persistent, structured context layer for AI agents to build world models and for humans to align, audit, and collaborate through natural language.
Facilitates secure, browser-based collaborative writing between humans and AI using Markdown, ensuring data remains on your local machine.
Manages product requirements documents with AI, optimizing context usage for significant token cost reductions.
Facilitates an AI-native community platform, enabling users to interact and manage community features entirely through natural conversation with their AI assistant.
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