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# TickTick MCP Service
> Bring TickTick task and goal data into MCP workflows with official OAuth and Open API support.
[Case study](https://aigalaxy.top/blog/ticktick-oauth-migration) · [AgentsGalaxy](https://aigalaxy.top/) · [GitHub](https://github.com/GalaxyXieyu/didatodolist-mcp)
`didatodolist-mcp` connects TickTick (Dida365) data to MCP-friendly workflows, with goal management, task analytics, and automation-friendly interfaces built on top of the official auth and API surface.
## Why it matters
- official OAuth 2.0 and Open API support makes the integration easier to trust
- MCP packaging makes task data more useful inside AI-native workflows
- the repo is suitable for productivity tooling, research flows, and personal operating systems
## Quick Start
1. Clone the repo and install Python dependencies.
2. Configure `.env` with your client credentials.
3. Run the OAuth helper once to get access and refresh tokens.
4. Start the MCP service and connect it to your editor or automation runtime.
## Tool List
## Features
### Goal Management
The goal management function allows users to create, track, and manage different types of goals:
- **Short-term goals**: Short-term goals with clear deadlines
- **Long-term goals**: Long-term and sustainable goals
- **Habitual goals**: Behavioral habits that need to be performed regularly
### Statistical Analysis
The statistical analysis function provides multi-dimensional analysis of task completion:
- **Time dimension**: Analyze task completion by day/week/month
- **Project dimension**: Classify tasks by project and count completion rate
- **Tag dimension**: Analyze task distribution by tag
### Keyword Extraction
Extract keywords from task content based on jieba segmentation library, support generating word cloud and heat analysis.
### Task-Goal Matching
Use content similarity and keyword matching algorithm to intelligently associate tasks with goals, helping users align daily tasks with long-term goals.
## Development Process
The project adopts a systematic development method and follows the following development stages:
1. **Planning stage**: Define project scope, functional requirements, and technical specifications
2. **Architecture design**: Complete the design of core data structure
3. **Basic function development**: Realize core API and data access layer
4. **Advanced function implementation**: Develop statistical analysis and goal matching algorithm
5. **Optimization and testing**: Improve performance and user experience
## Contribution
Welcome to submit issues and improvement suggestions! Please fork this repository and create a pull request.
## License
[MIT License](LICENSE)
Connection Info
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