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Provides a read-only Model Context Protocol server for Slack, supporting multi-user sessions and OAuth authentication over HTTP.
Facilitates multi-round AI expert debates by dynamically generating specialized AI agents to discuss any given topic.
Enables AI assistants to interact with Google Calendar for event management, scheduling, and availability checks.
Demonstrates how to build and deploy an intelligent customer service application using the MCP framework.
Enables multiple AI agents to collaborate through asynchronous messaging using the Model Context Protocol.
Generates comprehensive Learning Hour content and interactive whiteboard sessions for technical coaches to foster team technical excellence.
Provides a structured thinking environment for AI assistants to enhance reasoning.
Extends Teamwork.com with AI capabilities via the Model Context Protocol (MCP).
Analyzes GitHub Pull Requests using a Model-Controller-Presenter (MCP) architecture.
Enables interaction with Shortcut (formerly Clubhouse) through a Model Context Protocol (MCP) server.
Enables interaction with DingTalk through sending messages, retrieving conversation and user information, and managing calendar events.
Enables transcript retrieval, search, and summary generation from the Fireflies.ai API.
Manages project context persistently across coding sessions for developers.
Enables comprehensive interactions with the GitHub API for file operations, repository management, search, and more.
Enables AI assistants to interact with Fizzy project management data through the Model Context Protocol.
Manages resources within an Attio workspace through a FastMCP server.
Maintains a persistent, shared AI memory layer for teams, ensuring conversational context across sessions and collaborators.
Enables AI assistants to read, search, and modify Quip documents through a standardized Model Context Protocol (MCP) interface.
Facilitates structured, Slack-like agent-to-agent communication within Claude Code environments.
Facilitates multi-LLM deliberation where multiple LLMs collaboratively answer questions through peer review and synthesis.
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