mcpField
T
Toggl MCP Server

Toggl MCP Server

Local
github.com/taiseimiyajiProductivity22Updated 81w ago
Repository

About Toggl MCP Server

Toggl MCP Server is an MCP server that lets AI assistants manage time entries, projects and workspaces in the Toggl time tracking API through natural language. It is distributed as a Docker image with linux/amd64 and linux/arm64 support.

Key features

  1. 01Filter and retrieve time entries
  2. 02Create, update and delete time entries
  3. 03View and stop the running time entry
  4. 04Get workspaces and their projects

Use cases

  1. 01Logging or editing hours through an AI assistant
  2. 02Listing projects within a workspace
  3. 03Stopping a timer that is still running

MCP support

This project is an MCP server that exposes Toggl time entry, project and workspace operations to MCP clients, authenticated with TOGGL_API_TOKEN.

#toggl#time-tracking#ai-assistant

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "toggl": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "TOGGL_API_TOKEN",
        "lyricrime/toggl-mcp-server"
      ],
      "env": {
        "TOGGL_API_TOKEN": "<TOGGL_API_TOKEN>"
      }
    }
  }
}
Run commanddocker run -i --rm -e TOGGL_API_TOKEN lyricrime/toggl-mcp-server
Environment variables to set
TOGGL_API_TOKENSecret

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
ocilyricrime/toggl-mcp-server

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Toggl MCP Server

Read this in: English | 日本語

English

Overview

Toggl MCP Server is a Model Context Protocol (MCP) server that allows AI assistants to interact with the Toggl time tracking API. This enables AI assistants to manage time entries, projects, and workspaces in Toggl directly through natural language.

Features
  • Get time entries with filtering options
  • Create new time entries
  • Update existing time entries
  • Delete time entries
  • Get current running time entry
  • Stop running time entries
  • Get workspace information
  • Get projects in a workspace
  • Multi-platform support (linux/amd64, linux/arm64)
Usage
MCP Configuration

To connect an AI assistant to this server, configure the MCP connection in your .cursor/mcp.json file:

{
  "mcpServers": {
    "toggl-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "TOGGL_API_TOKEN",
        "lyricrime/toggl-mcp-server"
      ],
      "env": {
        "TOGGL_API_TOKEN": "your_toggl_api_token"
      }
    }
  }
}

Once configured, your AI assistant can interact with Toggl directly through natural language commands.

Environment Variables
  • TOGGL_API_TOKEN (required): Your Toggl API token for authentication
License

MIT License

日本語

概要

Toggl MCP Serverは、AIアシスタントがTogglのタイム・トラッキングAPIと対話できるようにするModel Context Protocol (MCP)サーバーです。これにより、AIアシスタントは自然言語を通じてTogglのタイムエントリー、プロジェクト、ワークスペースを直接管理できるようになります。

機能
  • フィルタリングオプション付きのタイムエントリー取得
  • 新規タイムエントリーの作成
  • 既存タイムエントリーの更新
  • タイムエントリーの削除
  • 現在実行中のタイムエントリーの取得
  • 実行中のタイムエントリーの停止
  • ワークスペース情報の取得
  • ワークスペース内のプロジェクト取得
  • マルチプラットフォームサポート（linux/amd64, linux/arm64）
使用方法
MCP設定

AIアシスタントをこのサーバーに接続するには、.cursor/mcp.jsonファイルにMCP接続を以下のように設定します：

{
  "mcpServers": {
    "toggl-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "TOGGL_API_TOKEN",
        "lyricrime/toggl-mcp-server"
      ],
      "env": {
        "TOGGL_API_TOKEN": "あなたのTogglAPIトークン"
      }
    }
  }
}

設定が完了すれば、AIアシスタントは自然言語コマンドを通じて直接Togglと対話できるようになります。

環境変数
  • TOGGL_API_TOKEN（必須）：認証用のToggl APIトークン
ライセンス

MITライセンス

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.