
ClickUp MCP Server
Provides integration with ClickUp's API, allowing you to retrieve task information and manage ClickUp data through MCP-compatible clients.
README
ClickUp MCP Server
A Model Context Protocol (MCP) server that provides integration with ClickUp's API, allowing you to retrieve task information and manage ClickUp data through MCP-compatible clients.
Features
- Task Retrieval: Get detailed information about specific ClickUp tasks
- List Management: Retrieve all tasks within a ClickUp list
- Comments Access: Fetch comments and discussion threads from tasks
Available Tools
get_task(taskID: str)
Retrieves comprehensive details about a specific ClickUp task including:
- Task name, description, and status
- Assignee information and due dates
- Priority levels and custom fields
- Creation and modification timestamps
get_tasks_by_listId(listID: str)
Fetches all tasks within a specified ClickUp list, providing:
- Complete task overview for project management
- Status tracking across multiple tasks
- Assignment and progress monitoring
- Workload distribution analysis
get_comments(taskID: str)
Retrieves all comments and discussion threads from a specific task:
- Complete comment history with timestamps
- Author information and reply threads
- Communication context and decision tracking
Prerequisites
- Python 3.11 or higher
- ClickUp API token
- UV package manager (recommended) or pip
Installation
-
Clone the repository:
git clone https://github.com/sksudeepvarma/clickup-mcp.git cd clickup-mcp-python
-
Install dependencies using UV:
uv sync
Or using pip:
pip install -r requirements.txt
-
Set up your ClickUp API token:
Create a
.env
file in the project root:CLICKUP_API_TOKEN=your_clickup_api_token_here
Or set the environment variable directly:
# Windows set CLICKUP_API_TOKEN=your_clickup_api_token_here # Linux/macOS export CLICKUP_API_TOKEN=your_clickup_api_token_here
Getting Your ClickUp API Token
- Log in to your ClickUp account
- Go to Settings → Apps
- Click Generate to create a new API token
- Copy the token and add it to your environment variables
Usage
Running the MCP Server
python main.py
Using with MCP Clients
Configure your MCP client (like Claude Desktop) to use this server by adding it to your MCP configuration:
{
"servers": {
"clickup-mcp": {
"type": "stdio",
"command": "python",
"args": ["path/to/clickup-mcp-python/main.py"],
"env": {
"CLICKUP_API_TOKEN": "${env:CLICKUP_API_TOKEN}"
}
}
}
}
Example Usage
Once connected to an MCP client, you can use the tools like:
# Get details about a specific task
get_task("86cyecyvu")
# Get all tasks in a list
get_tasks_by_listId("901607370347")
# Get comments from a task
get_comments("86cyecyvu")
Project Structure
clickup-mcp-python/
├── main.py # Main MCP server implementation
├── utilities.py # Helper functions for API token validation
├── pyproject.toml # Project dependencies and configuration
├── uv.lock # Locked dependency versions
├── README.md # This file
└── .vscode/
└── mcp.json # VS Code MCP configuration
Dependencies
- mcp[cli]: Model Context Protocol implementation
- requests: HTTP library for API calls
- python-dotenv: Environment variable management
Security
- API tokens are managed through environment variables
- No sensitive data is logged or stored
- Secure HTTPS communication with ClickUp API
推荐服务器

Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。