The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Ticktick MCP listing page.
A Model Context Protocol (MCP) server that provides tools for integrating TickTick task management tools. Using Python and the MCP SDK.
This repository contains a Model Context Protocol (MCP) server implementation for TickTick. It provides a standardized way for AI assistants and applications to interact with TickTick's task management functionality, allowing operations like:
With this MCP, AI systems can act as task masters to help manage your to-do lists and tasks in TickTick with natural language.
Clone this repository
Install dependencies
This MCP uses TickTick's OpenAPI scheme, which requires registering an app through TickTick's developer portal:
Manage Apps in the top right corner and login with your TickTick credentials+App Name buttonClient ID and Client SecretOAuth Redirect URL, enter a URL where you'll be redirected after authorization (e.g., http://127.0.0.1:8080)After registering your app, use the ticktick-py library to get your access token:
After authorizing, the access token will be saved to a .token-oauth file by default. You can extract the token from this file or use:
.env file in the root directory with your TickTick API key:
Run the MCP server:
This will start the MCP server on port 8000. You can now connect to it using any MCP client.
The server provides the following tools:
get_projects: Get a list of all projectsproject_details: Get details of a specific projectget_task_details: Get details of a specific taskcreate_project: Create a new projectcreate_task: Create a new task in a projectupdate_task: Update an existing taskcomplete_task: Mark a task as completedelete_task: Delete a taskOnce your MCP server is running, AI systems can help manage your tasks with natural language commands like:
This server can be used with any MCP-compatible client, such as:
To extend or modify this MCP server:
tools.pymain.py using mcp.add_tool()MIT
Contributions are welcome! Please feel free to submit a Pull Request.