Todoist MCP
Provides comprehensive task and project management capabilities including creating and managing tasks with natural language, organizing projects and sections, handling comments and labels, tracking completed tasks and productivity statistics, and managing collaboration features.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Todoist MCPAdd 'Schedule team meeting for Friday 3pm' to my Work project"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Todoist MCP
Note: predictably obsoleted by Todoist AI SDK, see here.
Connect this Model Context Protocol server to your LLM to interact with Todoist.
Functionality
This integration implements all the APIs available from the Todoist TypeScript Client, providing access to:
Task Management
Create tasks (with content, descriptions, due dates, priorities, labels, and more)
Create tasks with natural language (e.g., "Submit report by Friday 5pm #Work")
Retrieve tasks (individual, filtered, or all tasks)
Retrieve completed tasks (by completion date or due date)
Get productivity statistics
Update tasks
Move tasks (individually or in batches)
Close/reopen tasks
Delete tasks
Project Management
Create, retrieve, update, and delete projects
Section Management
Create, retrieve, update, and delete sections within projects
Comment Management
Add, retrieve, update, and delete comments for tasks or projects
Label Management
Create, retrieve, update, and delete labels
Manage shared labels
Collaboration
Get collaborators for projects
Related MCP server: Todoist MCP
Setup
Build the server app:
bun install
bun run buildRun in development
TODOIST_API_KEY=<key> bun devDocker deployment:
docker compose up -dDebugging:
Use the inspector to debug the server:
bunx @modelcontextprotocol/inspectorConfigure Claude:
You must install the Claude desktop app which supports MCP.
You can get your Todoist API key from Todoist > Settings > Integrations > Developer.
Then, in your claude_desktop_config.json, add a new MCP server:
{
"mcpServers": {
"default-server": {
"type": "streamable-http",
"url": "http://localhost:3000/mcp",
"note": "For Streamable HTTP connections, add this URL directly in your MCP Client"
}
}
}You can now launch Claude desktop app and ask to update Todoist.
🔐 Security Features
This MCP server has been secured with enterprise-grade security measures:
Authentication Required: JWT Bearer tokens or API key authentication
Rate Limiting: Prevents abuse with configurable limits
Input Validation: Comprehensive request validation and sanitization
Security Headers: CORS, CSP, HSTS, and other security headers
Logging & Monitoring: Security event logging and request monitoring
Environment Configuration: Secure configuration via environment variables
See SECURITY.md for detailed security documentation.
Quick Security Setup
Generate secure tokens:
npm run setup-securityCreate
.envfile:cp .env.example .env # Add your generated tokens and Todoist API keyRequired Environment Variables:
JWT_SECRET=your_generated_jwt_secret_here TODOIST_API_KEY=your_todoist_api_key_here
Authentication
The server supports two authentication methods:
Method 1: API Key (Recommended for scripts)
curl -X POST http://localhost:3000/mcp \
-H "X-API-Key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"jsonrpc": "2.0", "method": "tools/list", "id": 1}'Method 2: JWT Bearer Token (Recommended for applications)
curl -X POST http://localhost:3000/mcp \
-H "Authorization: Bearer YOUR_JWT_TOKEN" \
-H "Content-Type: application/json" \
-d '{"jsonrpc": "2.0", "method": "tools/list", "id": 1}'This server cannot be deployed
Maintenance
Related MCP Connectors
- mcpOAuthnet.todoist
Official Todoist MCP server for AI assistants to manage tasks, projects, and workflows.
Manage Superlist tasks and lists in plain language from any MCP-compatible AI agent.
Manage tasks, Focus Zone, notes, projects, and task history from compatible AI assistants.
AI-native task management: list, create, update and archive tasks with rich context for AI agents
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