Todoist MCP connector for AI agents

Tools your agent reaches for on Todoist MCP, scoped per user.

CALL ANY TOOL

Manage tasks, projects, labels, and filters in Todoist. Same toolkit, every framework, no auth plumbing.

todoistmcp_add-comments

Add-Comments

Add multiple comments to tasks or projects. Each comment must specify either taskId or projectId.

Parameters

Name

Type

Required

Description

comments

array

Required

The array of comments to add.

todoistmcp_add-filters

Add-Filters

todoistmcp_add-goals

Add-Goals

todoistmcp_add-labels

Add-Labels

todoistmcp_add-projects

Add-Projects

todoistmcp_add-reminders

Add-Reminders

todoistmcp_add-sections

Add-Sections

todoistmcp_add-tasks

Add-Tasks

todoistmcp_analyze-project-health

Analyze-Project-Health

todoistmcp_complete-goals

Complete-Goals

todoistmcp_complete-tasks

Complete-Tasks

todoistmcp_delete-object

Delete-Object

todoistmcp_fetch

Fetch

todoistmcp_fetch-object

Fetch-Object

todoistmcp_find-activity

Find-Activity

todoistmcp_find-comments

Find-Comments

todoistmcp_find-completed-tasks

Find-Completed-Tasks

todoistmcp_find-filters

Find-Filters

todoistmcp_find-goals

Find-Goals

todoistmcp_find-labels

Find-Labels

todoistmcp_find-project-collaborators

Find-Project-Collaborators

todoistmcp_find-projects

Find-Projects

todoistmcp_find-reminders

Find-Reminders

todoistmcp_find-sections

Find-Sections

todoistmcp_find-tasks

Find-Tasks

todoistmcp_find-tasks-by-date

Find-Tasks-By-Date

todoistmcp_get-overview

Get-Overview

todoistmcp_get-productivity-stats

Get-Productivity-Stats

todoistmcp_get-project-activity-stats

Get-Project-Activity-Stats

todoistmcp_get-project-health

Get-Project-Health

For more tools, view docs.

Build your Agent

Drop the toolkit in, point it at the user, and your agent can manage Todoist tasks and projects from the first run.

import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["todoistmcp"], toolNames: ["todoist_tasks_list", "todoist_task_create", "todoist_task_complete"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: z.object({}).passthrough(),
  func: async (args) => {
    const { data } = await sk.tools.executeTool({
      toolName: t.tool.definition.name,
      identifier: "user_123",
      params: args,
    });
    return JSON.stringify(data);
  },
}));

const agent = createReactAgent({ llm, tools: lcTools });

import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["todoistmcp"], toolNames: ["todoist_tasks_list", "todoist_task_create", "todoist_task_complete"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  type: "function",
  function: {
    name: t.tool.definition.name,
    description: t.tool.definition.description,
    parameters: t.tool.definition.input_schema,
  },
}));

const resp = await openai.responses.create({
  model: "gpt-4o", input: prompt, tools: llmTools,
});

import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["todoistmcp"], toolNames: ["todoist_tasks_list", "todoist_task_create", "todoist_task_complete"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

import { Agent } from "@google/adk/agents";
import {
  MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
  connectionParams: new StreamableHTTPConnectionParams({
    url: "https://mcp.scalekit.com/todoistmcp",
    headers: { Authorization: `Bearer ${userScopedToken}` },
  }),
});

const agent = new Agent({
  name: "agent", model: "gemini-2.0-flash",
  tools: await toolset.getTools(),
});

Try these prompts

Paste any prompt into your productivity agent to start managing Todoist tasks.

Search & recall

Copy the prompt

Copied

List all priority-1 tasks due this week.

Copy the prompt

Copied

Show all overdue tasks in my Todoist.

Copy the prompt

Copied

List all tasks labeled [label] across all projects.

Action & create

Copy the prompt

Copied

Create a task: [title] due [date] with priority [1] in [project].

Copy the prompt

Copied

Mark task [ID] as complete.

Copy the prompt

Copied

List all projects in my Todoist and their task counts.

SEE HOW AUTH WORKS

Users authorize Todoist once. Their credentials stay vaulted, every task action runs under their identity, and every call is logged.

Authorize

Your user connects

Todoist MCP

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

Limited to user

Store

Their

Todoist MCP

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

Resolve

When your agent calls a

Todoist MCP

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

Audit

Every

Todoist MCP

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

Same per-user auth pattern across other productivity and project management connectors.

OPS

Email-to-calendar scheduling agent

Parse scheduling intent from Gmail threads and create Google Calendar events with the right attendees and timezone.

Gmail

Google Calendar

View agent

ENGINEERING

Engineering standup agent

Aggregate GitHub and GitLab activity, link to Jira, and post a daily standup digest to Slack. No async updates.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with user identity, scope enforcement, and audit.

01.

Shared tokens break per-user analytics

A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential so attribution, audit, and scope stay accurate.

// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Todoist MCP today. Others tomorrow.

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

FAQs

Frequently Asked Questions

Does the agent access Todoist as the user or a shared account?

As the user. Each person authorizes once and all task actions are attributed to that user's Todoist account.

Where is the Todoist OAuth token stored?

In Scalekit's AES-256 vault, namespaced per tenant. Tokens never appear in prompts or LLM context.

Can I restrict the agent to read-only task access?

Yes. Use listScopedTools to allow task listing without granting creation or completion.

What happens when a user revokes Todoist access?

The connection is invalidated on the next tool call. Subsequent requests fail closed.

Can the agent create Todoist tasks from Slack messages in one workflow?

Yes. A single agent can read Slack messages and create Todoist tasks in the same workflow, using the same user identity.

On this page

Tool and actions Framework Compatibility Starter Prompt Auth Lifecycle Try Other Agents Why Scalekit