didatodolist-mcp

# 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)