TickTick MD MCP
Enables AI integration with TickTick to manage tasks, projects, and tags through the Model Context Protocol. It allows users to import from Markdown, export project data to multiple formats, and perform task operations using natural language.
README
ticktick-md-mcp
A Python tool for importing and exporting TickTick notes and tasks to Markdown and JSON formats.
Three ways to use:
- 🖥️ Terminal CLI - Interactive command-line interface
- 🐍 Python Library - Import and use in your own code
- 🤖 MCP Server - AI integration with Claude (via Model Context Protocol)
Features
- OAuth 2.0 authentication with TickTick
- Import tasks from Markdown files with tags and project assignment
- Export tasks filtered by tag or entire projects
- Multiple export formats (Markdown, JSON)
- Automatic date extraction from task titles
- Extensible exporter architecture
- 🆕 MCP Server - Expose functionality to Claude AI via Model Context Protocol
Installation
Prerequisites
- Python 3.11 or higher
- TickTick account
- TickTick OAuth app credentials (Setup Guide)
Install Dependencies
# Using pip
pip install -r requirements.txt
# Or using uv (recommended)
uv pip install -r requirements.txt
Quick Start
1. Set Up Environment Variables
Create a .env file in the project root:
cp .env.example .env
Edit .env and add your TickTick OAuth credentials:
TICKTICK_CLIENT_ID=your_client_id_here
TICKTICK_CLIENT_SECRET=your_client_secret_here
TICKTICK_REDIRECT_URI=http://localhost:8000/auth/ticktick/callback
TICKTICK_ACCESS_TOKEN=your_access_token_here
2. Get Access Token
Run the token helper to authenticate:
python -m src.utils.get_token
Follow the prompts to:
- Visit the authorization URL
- Authorize the app
- Copy the authorization code from the redirect URL
- The script will save your access token to
.env
3. Run the CLI
python ticktick_cli.py
The interactive menu provides all functionality:
- Import tasks from Markdown - Create tasks from markdown files with tags
- Export notes by tag - Filter and export tasks by tag
- Export entire projects - Export all tasks from a project
- List tags in a project - See available tags
- Exit
Choose your output format (Markdown or JSON) when exporting.
Usage
The CLI provides a unified interface for all operations:
Importing Tasks from Markdown
Run the CLI and select option 1:
python ticktick_cli.py
# Then select: 1. Import tasks from Markdown
You'll be prompted to:
- Enter the markdown file path
- Choose a default project (or leave empty for Inbox)
- Preview (dry run) or import directly
Markdown Format
Tasks should be formatted as bullet lists with checkboxes:
- [ ] Review pull request
Tags: code-review, urgent
Project: Work Tasks
Check for performance issues and security
- [ ] Buy groceries
Tags: shopping, personal
Milk, bread, eggs
- [x] Completed task
Tags: done
Metadata Fields:
- Tags: Comma-separated list of tags (e.g.,
Tags: work, urgent) - Project: TickTick project/list name (e.g.,
Project: Work Tasks) - Description: Multi-line task description (any non-metadata text after the title)
Task States:
- [ ]- Unchecked task (will be imported)- [x]- Completed task (skipped during import)
Exporting Tasks
Run the CLI and select option 2 or 3:
python ticktick_cli.py
# Then select:
# 2. Export notes by tag
# 3. Export entire project
Export options:
- Choose between Markdown or JSON format
- Specify output filename
Programmatic Usage
If you need to use the functionality in your own Python scripts:
from src.exporters.notes_exporter import NotesManager, MarkdownExporter, JSONExporter
from src.importers.task_importer import TaskImporter
# Initialize
manager = NotesManager()
# Export by tag (Markdown)
manager.export_by_tag(
tag_name="journal",
output_file="journal.md",
project_name="On My Mind"
)
# Export by tag (JSON)
manager.export_by_tag(
tag_name="work",
output_file="work_notes.json",
project_name="Work Tasks",
exporter=JSONExporter()
)
# Export entire project
manager.export_project(
project_name="Personal",
output_file="personal_notes.md"
)
# List all projects
manager.list_projects()
# List tags in a project
manager.list_tags(project_name="On My Mind")
API Usage
Use the TickTick API wrapper directly:
from src.api.ticktick_api import TickTickAPI, TickTickOAuth
import os
# Initialize API
api = TickTickAPI(os.getenv('TICKTICK_ACCESS_TOKEN'))
# Get user info
user = api.get_user_info()
# Get all projects
projects = api.get_projects()
# Get project data with tasks
project_data = api.get_projects_data(project_id)
# Create a task
task = api.create_task(
title="New Task",
content="Task description",
project_id="project_id_here",
tags=["work", "urgent"]
)
MCP Server - AI Integration
The MCP (Model Context Protocol) server allows Claude AI to directly interact with your TickTick account for importing and exporting tasks.
What is MCP?
MCP is a protocol that lets AI assistants like Claude access external tools and data sources. With the TickTick MCP server, you can ask Claude to:
- "Export my work tasks to markdown"
- "Import these tasks to my Home project"
- "List all my TickTick projects"
- "Create a task in my Work project"
Quick Start
-
Install MCP dependency:
pip install mcp>=1.0.0 -
The
.mcp.jsonfile is already configured in the project root - no setup needed! -
Set your access token:
export TICKTICK_ACCESS_TOKEN=your_token_here -
Use with Claude Code:
- Open this project in Claude Code
- The MCP server will be automatically detected
- Ask Claude to interact with TickTick!
Available MCP Tools
The server exposes these tools to Claude:
list-projects
List all your TickTick projects/lists.
Example: "Show me all my TickTick projects"
list-tags
List all tags in a specific project.
Parameters:
- project_name: Name of the project
Example: "What tags are in my Work Tasks project?"
export-by-tag
Export tasks filtered by a specific tag.
Parameters:
- tag_name: Tag to filter by
- project_name: Project to export from
- format: 'markdown' or 'json' (default: markdown)
Example: "Export all tasks tagged 'urgent' from Work Tasks"
export-project
Export all tasks from an entire project.
Parameters:
- project_name: Project to export
- format: 'markdown' or 'json' (default: markdown)
Example: "Export my Personal project as markdown"
import-from-markdown
Import tasks from markdown content into TickTick.
Parameters:
- markdown_content: Markdown formatted task list
- project_name: Target project (defaults to Inbox)
- dry_run: Preview without creating (default: true)
Example: "Import these tasks to my Work project:
- [ ] Review documentation
Tags: work, urgent
- [ ] Schedule meeting
Tags: work"
create-task
Create a single task in TickTick.
Parameters:
- title: Task title
- project_name: Project to add to (optional)
- content: Task description (optional)
- tags: List of tags (optional)
Example: "Create a task 'Buy groceries' with tags shopping and personal"
Example Claude Conversations
Export tasks:
You: "Export all my work tasks tagged 'urgent' as markdown"
Claude: *uses export-by-tag tool*
Claude: "Here are your urgent work tasks: [markdown content]"
Import tasks:
You: "Here's a list of tasks, import them to my Home project:
- [ ] Fix leaky faucet
Tags: home, maintenance
- [ ] Plant garden
Tags: home, weekend"
Claude: *uses import-from-markdown tool with dry_run=false*
Claude: "I've imported 2 tasks to your Home project"
List and organize:
You: "What projects do I have in TickTick?"
Claude: *uses list-projects tool*
Claude: "You have 5 projects: Work Tasks, Personal, Home Tasks, Shopping, Ideas"
MCP Configuration
The .mcp.json file in the project root is pre-configured:
{
"mcpServers": {
"ticktick": {
"command": "python3",
"args": ["ticktick_mcp_server.py"]
}
}
}
Make sure TICKTICK_ACCESS_TOKEN is set in your environment or .env file.
For Claude Desktop (if you use it), add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"ticktick": {
"command": "python3",
"args": ["/absolute/path/to/pyTickTick/ticktick_mcp_server.py"],
"env": {
"TICKTICK_ACCESS_TOKEN": "your_token_here"
}
}
}
}
Architecture
The MCP server reuses all existing code:
- Same API wrapper (
src/api/ticktick_api.py) - Same import logic (
src/importers/) - Same export logic (
src/exporters/)
No duplication - the MCP server is a thin wrapper that exposes existing functionality to Claude via the Model Context Protocol.
Troubleshooting MCP
"Server not detected":
- Ensure
.mcp.jsonexists in project root - Check that
TICKTICK_ACCESS_TOKENis set in environment - Restart Claude Code
"Authentication failed":
- Run
python -m src.utils.get_tokento get a fresh token - Update
.envfile with new token - Set environment variable:
export TICKTICK_ACCESS_TOKEN=token
"Import not working":
- Set
dry_run=Falsein the import command - Default is
dry_run=Truefor safety (preview mode)
Export Formats
Markdown
Exports tasks as a formatted Markdown document:
# Journal Notes
Exported on January 03, 2026 at 07:31 PM
Total notes: 7
---
## 2025-12-31 My Note Title
**Date:** December 31, 2025
Note content here...
---
Features:
- Tasks sorted by date (most recent first)
- Dates extracted from task titles (format:
YYYY-MM-DD) - Fallback to API dates if available
- Metadata header with export info
JSON
Exports tasks as structured JSON:
{
"metadata": {
"exported_at": "2026-01-03T19:31:00.123456",
"total_tasks": 7,
"tag_name": "journal",
"project_name": "On My Mind"
},
"tasks": [
{
"id": "...",
"title": "2025-12-31 My Note",
"content": "Note content...",
"tags": ["journal"],
...
}
]
}
Date Handling
The exporter intelligently handles dates:
-
From Title - Extracts dates from titles like
2025-12-31 Task Name- Supports optional leading whitespace
- Format:
YYYY-MM-DDat the start of the title - Displays as: "December 31, 2025"
-
From API - Falls back to
modifiedTimeorcreatedTimeif available- Note: TickTick's Open API may not return these fields for notes
-
No Date - Shows "No date" if neither source is available
Utilities
Get Token Helper
Quickly get a new OAuth access token:
python -m src.utils.get_token
Debug Tags
Inspect task structure and find tags:
python -m src.utils.debug_tags
Shows:
- All projects in your account
- Sample task structures
- Tasks containing specific keywords
- Available tags
Project Structure
pyTickTick/
├── 🚀 ticktick_cli.py # Main CLI - Interactive terminal interface
├── 🤖 ticktick_mcp_server.py # MCP Server - AI integration with Claude
│
├── src/ # Source code (organized by function)
│ ├── api/
│ │ └── ticktick_api.py # TickTick API wrapper (OAuth + API client)
│ │
│ ├── exporters/
│ │ └── notes_exporter.py # Export tasks to Markdown/JSON
│ │
│ ├── importers/
│ │ ├── task_importer.py # Import tasks from Markdown
│ │ └── markdown_parser.py# Parse markdown task format
│ │
│ └── utils/
│ ├── get_token.py # OAuth token helper
│ └── debug_tags.py # Task structure debugger
│
├── tests/ # Comprehensive test suite
│ ├── conftest.py # Pytest fixtures and configuration
│ ├── test_markdown_parser.py
│ ├── test_task_importer.py
│ ├── test_notes_exporter.py
│ └── test_ticktick_api.py
│
├── 📝 Config:
│ ├── .env.example # Template for credentials (copy to .env)
│ ├── .mcp.json # MCP server configuration for Claude
│ ├── requirements.txt # Python dependencies
│ ├── pyproject.toml # Project metadata
│ └── pytest.ini # Pytest configuration
│
├── LICENSE # MIT License
└── README.md # This file
Three Ways to Use:
- Terminal CLI: Run
python ticktick_cli.pyfor interactive interface - Python Library: Import and use
src/modules in your own code - MCP with Claude: Open project in Claude Code - MCP auto-detected!
Getting Started:
- Run
python -m src.utils.get_tokenonce to authenticate - CLI: Run
python ticktick_cli.pyfor all import/export operations - MCP: Open project in Claude Code and ask Claude to interact with TickTick!
- (Optional) Run
python -m src.utils.debug_tagsto inspect task structures
TickTick OAuth Setup
1. Create a TickTick Developer App
- Go to TickTick Developer Portal
- Sign in with your TickTick account
- Create a new app
- Set the Redirect URI to:
http://localhost:8000/auth/ticktick/callback - Copy your Client ID and Client Secret
2. Configure Environment
Add your credentials to .env:
TICKTICK_CLIENT_ID=your_client_id_from_developer_portal
TICKTICK_CLIENT_SECRET=your_client_secret_from_developer_portal
TICKTICK_REDIRECT_URI=http://localhost:8000/auth/ticktick/callback
3. Get Access Token
Run python -m src.utils.get_token and follow the prompts.
Troubleshooting
"No access token found"
Run python -m src.utils.get_token to authenticate and get a new token.
"Failed to fetch projects"
- Verify your access token is valid
- Check that your OAuth app has the correct scopes:
tasks:read tasks:write - Try getting a new token with
python -m src.utils.get_token
"No date" showing for all tasks
- The TickTick Open API doesn't return
createdTime/modifiedTimefor notes - Add dates to your task titles in format
YYYY-MM-DD Task Name - The exporter will automatically extract and format these dates
Import errors
Make sure all dependencies are installed:
pip install -r requirements.txt
Development
Testing
The project includes a comprehensive test suite using pytest.
Running Tests
# Install test dependencies
pip install -r requirements.txt
# Run all tests
pytest
# Run with coverage report
pytest --cov=src --cov-report=html
# Run specific test file
pytest tests/test_markdown_parser.py
# Run tests with specific marker
pytest -m unit # Run only unit tests
pytest -m integration # Run only integration tests
pytest -m api # Run only API tests
# Run with verbose output
pytest -v
Test Structure
- tests/conftest.py - Shared fixtures and test configuration
- tests/test_markdown_parser.py - Tests for markdown parsing (40+ tests)
- tests/test_task_importer.py - Tests for task import functionality
- tests/test_notes_exporter.py - Tests for export functionality
- tests/test_ticktick_api.py - Tests for API wrapper
Test Coverage
The test suite covers:
- Markdown parsing with various formats
- Task import with dry-run and actual creation
- Export to Markdown and JSON formats
- API wrapper with mocked responses
- Edge cases and error handling
- Project and tag filtering
Writing New Tests
# tests/test_example.py
import pytest
@pytest.mark.unit
def test_example(sample_markdown_content):
"""Test description"""
# Your test code here
assert True
Available fixtures:
sample_markdown_content- Sample markdown for testingmock_ticktick_api- Mocked API instancemock_env_token- Mocked environment tokensample_tasks- Sample task data
File Responsibilities
- src/api/ticktick_api.py - Pure API wrapper, no business logic
- src/exporters/notes_exporter.py - Export logic and functionality
- src/importers/task_importer.py - Import logic and functionality
- src/utils/get_token.py - Simple OAuth flow helper
- src/utils/debug_tags.py - Debugging and inspection utility
- ticktick_cli.py - Main unified CLI interface
Adding New Export Formats
Extend the BaseExporter class:
from src.exporters.notes_exporter import BaseExporter
class CSVExporter(BaseExporter):
def export(self, tasks, output_file, metadata):
# Your CSV export logic here
pass
Then use it:
manager.export_by_tag(
tag_name="notes",
output_file="notes.csv",
exporter=CSVExporter()
)
Requirements
- python-dotenv >= 1.1.1
- requests >= 2.26.0
See requirements.txt for complete list.
License
MIT License - see LICENSE for details.
Contributing
Contributions are welcome! Please feel free to submit issues and pull requests.
Roadmap
Future ideas under consideration:
- Full CLI interface with Click
- Additional export formats (CSV, HTML, PDF)
- Task management features (update, delete, complete)
- Configuration system
- Package distribution on PyPI
Support
For issues or questions:
- Check the Troubleshooting section
- Review the TickTick API Documentation
- Open an issue (once repository is public)
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。