What is the ClickUp MCP Server?

The ClickUp MCP Server is an enhanced integration that connects your ClickUp task management system with AI assistants via the Model Context Protocol (MCP), enabling AI to interact with and manage your tasks, comments, and checklists.

What enhancements does this version offer over the original?

This enhanced version includes support for Task Relationships (dependencies, links, tags), a comprehensive Comments API, Checklists API support, and architectural improvements like a service-based architecture and better TypeScript type definitions.

How do I set up the ClickUp MCP Server locally?

1. Clone the repository.
2. Install dependencies with npm install.
3. Copy .env.example to .env and add your ClickUp API key and team ID.
4. Build the project with npm run build.
5. Run the server with node build/index.js.

How do I deploy the ClickUp MCP Server on Railway?

1. Fork or clone the repository to your GitHub account.
2. Create a new project on Railway from your GitHub repository.
3. Add CLICKUP_API_KEY and CLICKUP_TEAM_ID as environment variables in Railway.
4. Deploy the project.

How do I connect the server to Claude?

1. Open the Claude Desktop app.
2. Go to Settings > MCP Servers.
3. Add a new MCP server with the provided JSON configuration, replacing the URL and credentials with your own.

What is UBOS and how does it relate to the ClickUp MCP Server?

UBOS is a full-stack AI Agent Development Platform that helps you orchestrate AI Agents, connect them with your enterprise data, and build custom AI Agents. The ClickUp MCP Server seamlessly integrates with UBOS to unlock advanced AI-powered task management capabilities.

Can I add new tools to the ClickUp MCP Server?

Yes, the server is designed to be extensible. You can add new ClickUp API tools by defining types, implementing service methods, defining tool schemas, and implementing tool handlers.

What are some use cases for the ClickUp MCP Server?

Use cases include project management, product development, content calendars, customer support, and sales & marketing, all benefiting from AI-enhanced task management.

What kind of support do the Task Relationships APIs offer?

The Task Relationships APIs support creating and managing dependencies between tasks, linking related tasks, and organizing tasks with tags.

How can the Comments API be used effectively?

The Comments API supports adding and retrieving comments on tasks, lists, and chat views, with support for threaded comments and comment operations like updating and deleting comments. An AI agent can summarize comments, identify key discussion points, and even suggest responses.

What can I do with the Checklists API?

The Checklists API allows you to create and manage checklists on tasks, including creating, editing, and deleting checklists and checklist items. Great for breaking down large task into subtasks.