# Todoist AI and MCP SDK

Library for connecting AI agents to Todoist. Includes tools that can be integrated into LLMs,
enabling them to access and modify a Todoist account on the user's behalf.

These tools can be used both through an MCP server, or imported directly in other projects to
integrate them to your own AI conversational interfaces.

## Using tools

### 1. Add this repository as a dependency

```sh
npm install @doist/todoist-ai
```

### 2. Import the tools and plug them to an AI

Here's an example using [Vercel's AI SDK](https://ai-sdk.dev/docs/ai-sdk-core/generating-text#streamtext).

```js
import { findTasksByDate, addTasks } from "@doist/todoist-ai";
import { TodoistApi } from "@doist/todoist-api-typescript";
import { streamText } from "ai";

// Create Todoist API client
const client = new TodoistApi(process.env.TODOIST_API_KEY);

// Helper to wrap tools with the client
function wrapTool(tool, todoistClient) {
    return {
        ...tool,
        execute(args) {
            return tool.execute(args, todoistClient);
        },
    };
}

const result = streamText({
    model: yourModel,
    system: "You are a helpful Todoist assistant",
    tools: {
        findTasksByDate: wrapTool(findTasksByDate, client),
        addTasks: wrapTool(addTasks, client),
    },
});
```

## Using as an MCP server

### Quick Start

You can run the MCP server directly with npx:

```bash
npx @doist/todoist-ai
```

### Setup Guide

The Todoist AI MCP server is available as a streamable HTTP service for easy integration with various AI clients:

**Primary URL (Streamable HTTP):** `https://ai.todoist.net/mcp`

#### Claude Desktop

1. Open Settings → Connectors → Add custom connector
2. Enter `https://ai.todoist.net/mcp` and complete OAuth authentication

#### Cursor

Create a configuration file:
- **Global:** `~/.cursor/mcp.json`
- **Project-specific:** `.cursor/mcp.json`

```json
{
  "mcpServers": {
    "todoist": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://ai.todoist.net/mcp"]
    }
  }
}
```

Then enable the server in Cursor settings if prompted.

#### Claude Code (CLI)

Firstly configure Claude so it has a new MCP available using this command:

```bash
claude mcp add --transport http todoist https://ai.todoist.net/mcp
```

Then launch `claude`, execute `/mcp`, then select the `todoist` MCP server.

This will take you through a wizard to authenticate using your browser with Todoist. Once complete you will be able to use todoist in `claude`.


#### Visual Studio Code

1. Open Command Palette → MCP: Add Server
2. Select HTTP transport and use:

```json
{
  "servers": {
    "todoist": {
      "type": "http",
      "url": "https://ai.todoist.net/mcp"
    }
  }
}
```

#### Other MCP Clients

```bash
npx -y mcp-remote https://ai.todoist.net/mcp
```

For more details on setting up and using the MCP server, including creating custom servers, see [docs/mcp-server.md](docs/mcp-server.md).

## Features

A key feature of this project is that tools can be reused, and are not written specifically for use in an MCP server. They can be hooked up as tools to other conversational AI interfaces (e.g. Vercel's AI SDK).

This project is in its early stages. Expect more and/or better tools soon.

Nevertheless, our goal is to provide a small set of tools that enable complete workflows, rather than just atomic actions, striking a balance between flexibility and efficiency for LLMs.

For our design philosophy, guidelines, and development patterns, see [docs/tool-design.md](docs/tool-design.md).

### Available Tools

For a complete list of available tools, see the [src/tools](src/tools) directory.

#### OpenAI MCP Compatibility

This server includes `search` and `fetch` tools that follow the [OpenAI MCP specification](https://platform.openai.com/docs/mcp), enabling seamless integration with OpenAI's MCP protocol. These tools return JSON-encoded results optimized for OpenAI's requirements while maintaining compatibility with the broader MCP ecosystem.

## Dependencies

-   MCP server using the official [@modelcontextprotocol/sdk](https://github.com/modelcontextprotocol/typescript-sdk?tab=readme-ov-file#installation)
-   Todoist Typescript API client [@doist/todoist-api-typescript](https://github.com/Doist/todoist-api-typescript)

## MCP Server Setup

See [docs/mcp-server.md](docs/mcp-server.md) for full instructions on setting up the MCP server.

## Local Development Setup

See [docs/dev-setup.md](docs/dev-setup.md) for full instructions on setting up this repository locally for development and contributing.

### Widgets

This project includes support for **MCP Apps** – interactive UI widgets rendered inline in AI chat interfaces. Widgets provide rich visual representations of tool outputs (e.g., task lists) instead of plain text.

See [docs/widgets.md](docs/widgets.md) for the widget architecture, build pipeline, and development workflow.

### Quick Start

After cloning and setting up the repository:

- `npm start` - Build and run the MCP inspector for testing
- `npm run dev` - Development mode with auto-rebuild and restart

## Releasing

This project uses [release-please](https://github.com/googleapis/release-please) to automate version management and package publishing.

### How it works

1. Make your changes using [Conventional Commits](https://www.conventionalcommits.org/):

    - `feat:` for new features (minor version bump)
    - `fix:` for bug fixes (patch version bump)
    - `feat!:` or `fix!:` for breaking changes (major version bump)
    - `docs:` for documentation changes
    - `chore:` for maintenance tasks
    - `ci:` for CI changes

2. When commits are pushed to `main`:

    - Release-please automatically creates/updates a release PR
    - The PR includes version bump and changelog updates
    - Review the PR and merge when ready

3. After merging the release PR:
    - A new GitHub release is automatically created
    - A new tag is created
    - The `publish` workflow is triggered
    - The package is published to npm
