What is the difference between V1 and V2 tools?
V1 tools use the official Dida365/TickTick Open API and cover standard task and project operations. V2 tools use private APIs that enable extras like tags, habits, folders, and search.
@linhai0872
基本情報
MCP server that connects AI agents (Claude Code, Cursor, Codex) to Dida365/TickTick. 40 tools for tasks, projects, tags, habits, folders, and search — manage everything through natural language.
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"dida365": {
"command": "uvx",
"args": [
"dida365-agent-mcp"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。
dida365 Agent Mcp is an MCP (Model Context Protocol) server that provides 40 tools to interact with the Dida365 (China) and TickTick (International) task management platforms. It combines official V1 Open API tools with private V2 API tools for advanced features like tags, habits, folders, and parent tasks. Designed for developers and power users who want to automate task management via AI agents.
Run uvx --from dida365-agent-mcp dida365-oauth once to authenticate via a one-click browser OAuth flow. Then add the server to your MCP client configuration using uvx dida365-agent-mcp as the command and args. The token is saved locally and lasts approximately 180 days.
docker compose up commandV1 tools use the official Dida365/TickTick Open API and cover standard task and project operations. V2 tools use private APIs that enable extras like tags, habits, folders, and search.
Run `uvx --from dida365-agent-mcp dida365-oauth` once. This opens a browser for OAuth login and saves the token locally for repeated use (valid ~180 days).
Yes. Set one environment variable to switch between Dida365 (China) and TickTick (International). Both platforms are fully supported.
Two transports are supported: `stdio` for local client integration and `streamable-http` for remote agent connections.
The server requires `uvx` to run. Docker is optional but recommended for simplified deployment via `docker compose up`.

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