Clockify MCP Server
Enables AI assistants to interact with Clockify time tracking API to manage time entries, projects, tasks, and workspaces through natural language commands.
README
Clockify MCP Server
A Model Context Protocol (MCP) server that provides seamless integration with Clockify time tracking API. This server enables AI assistants to interact with Clockify to manage time entries, projects, tasks, and workspaces.
Features
- 🕐 Time Entry Management: Create, update, delete, and list time entries
- 📁 Project Management: Browse and search projects across workspaces
- ✅ Task Management: Access and manage tasks within projects
- 👤 User Profile: Retrieve user information and workspace details
- 🏢 Workspace Management: List and navigate between workspaces
Demonstration

Available Tools
User Management
get-clockify-user- Retrieve current user profile information
Workspace Management
list-clockify-workspaces- List all accessible workspaces
Project Management
list-clockify-projects- List projects in a workspace with optional name filtering
Task Management
list-clockify-tasks- List tasks within a specific project
Time Entry Management
create-clockify-time-entry- Create new time entriesupdate-clockify-time-entry- Update existing time entriesdelete-clockify-time-entry- Delete time entrieslist-clockify-time-entries- List time entries with date filtering
Prerequisites
- Clockify Account: You need a Clockify account with API access
- API Key: Generate your Clockify API key from your profile settings
- MCP-Compatible Client: VS Code with GitHub Copilot, Claude Desktop, or other MCP clients
Installation
Option 1: Using NPX (Recommended)
Add the following configuration to your MCP client:
{
"servers": {
"mcp_clockify": {
"command": "npx",
"args": ["-y", "mcp_clockify@latest"],
"env": {
"CLOCKIFY_API_KEY": "your-clockify-api-key-here"
}
}
}
}
Option 2: Local Development
-
Clone the repository:
git clone <repository-url> cd clockify-mcp -
Install dependencies:
npm install -
Build the project:
npm run build -
Configure your MCP client:
{ "servers": { "mcp_clockify": { "command": "node", "args": ["/path/to/clockify-mcp/build/index.js"], "env": { "CLOCKIFY_API_KEY": "your-clockify-api-key-here" } } } }
Configuration
Getting Your Clockify API Key
- Log in to your Clockify account
- Go to Profile Settings (click your avatar in the top-right corner)
- Navigate to the API section
- Generate or copy your existing API key
VS Code Setup
- Open VS Code
- Run the command
MCP: Open User Configuration(Ctrl/Cmd + Shift + P) - This opens or creates the
mcp.jsonfile in your user profile - Add the configuration with your API key:
{
"servers": {
"mcp_clockify": {
"command": "npx",
"args": ["-y", "mcp_clockify@latest"],
"env": {
"CLOCKIFY_API_KEY": "your-clockify-api-key-here"
}
}
}
}
- Save the file and restart VS Code
Claude Desktop Setup
Add to your Claude Desktop configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"mcp_clockify": {
"command": "npx",
"args": ["-y", "mcp_clockify@latest"],
"env": {
"CLOCKIFY_API_KEY": "your-clockify-api-key-here"
}
}
}
}
Gemini CLI Setup
- Open your Gemini CLI configuration file (e.g.,
~/.gemini/settings.json). - Add the following configuration:
{
"mcpServers": {
"mcp_clockify": {
"command": "npx",
"args": ["-y", "mcp_clockify@latest"],
"env": {
"CLOCKIFY_API_KEY": "your-clockify-api-key-here"
}
}
}
}
Usage Examples
Creating a Time Entry
I worked on the Research project for Acme Corp workspace from today 9 AM to 5 PM. Please create a time entry for this work session in Clockify.
Listing Recent Time Entries
Show me my time entries for this week in Clockify.
Managing Projects
List all projects in my main workspace and help me find the "Website Redesign" project.
Daily Time Tracking
I need to log 3 hours of work on the Mobile App project from 2 PM to 5 PM today with the description "Bug fixes and testing".
Development
Scripts
npm run build- Build the TypeScript projectnpm start- Start the servernpm run inspect- Use MCP inspector for debugging
Project Structure
├── src/
│ └── index.ts # Main server implementation
├── build/
│ └── index.js # Compiled JavaScript
├── package.json # Project configuration
├── tsconfig.json # TypeScript configuration
└── README.md # This file
Troubleshooting
Common Issues
-
Invalid API Key Error
- Verify your API key is correct and has proper permissions
- Check that the environment variable is properly set
-
Network Connection Issues
- Ensure you have internet connectivity
- Verify Clockify API is accessible from your network
-
Server Not Starting
- Check that Node.js is installed (version 16 or higher)
- Verify all dependencies are installed with
npm install
Debug Mode
Use the MCP inspector for debugging:
npm run inspect
This opens a web interface to test and debug the MCP server.
API Reference
The server interacts with Clockify API v1. For detailed API documentation, visit Clockify API Documentation.
Contributing
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests if applicable
- Submit a pull request
License
This project is licensed under the ISC License.
Support
For issues and questions:
- Check the troubleshooting section
- Review Clockify API documentation
- Open an issue on the repository
Note: This is an unofficial integration.
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。