"Tools for Managing Tasks and Schedules with Apps Like TickTick and Todo" matching MCP servers:

Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.

A Todo app leveraging MCP Apps to provide interactive UI within conversations, allowing users to manage todos via Claude Desktop or Claude Code.

Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.

Enables LLMs to read and write local user data, generate fake users via sampling, and interact with structured prompts and resources.

MCP server enabling AI agents to interact with OpenProject API v3 for project management, including creating and managing work packages, projects, comments, time entries, boards, and user dashboards.

Enables AI agents to inspect any public domain’s web technology stack, email deliverability and DNS health (MX, SPF, DKIM, DMARC, MTA-STS, TLS-RPT, BIMI), and HTTPS security posture with typed JSON scores and remediation-ready findings, plus history, bulk checks, monitoring alerts, and reverse technology lookups.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

Enables AI agents to search, filter, and monitor curated AI model API free credits and low-cost token promotions through eight tools, four resources, and three prompts — including finding deals expiring soon, checking eligibility and blocked conditions, and producing daily briefs.

A Model Context Protocol (MCP) server that connects AI assistants to your TickTick tasks, enabling task management through natural language.

A streamlined MCP server for XMP metadata embedding with beautiful formatting and smart filename indicators, enabling metadata embedding, reading, validation, and report generation for lifestyle, product, and orbit schemas.

An MCP server that lets your AI assistant manage TickTick tasks, projects, habits, and more through natural language, supporting both TickTick and Dida365.

Enables AI assistants to read and write TickTick/Dida365 tasks, projects, tags, habits, and calendars through the service's private web API using browser session-cookie authentication. It exposes 34 tools covering task creation, completion, search, batch operations, trash restore, and full-account delta sync.

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

An MCP server that enables Claude to manage TickTick tasks and projects via OAuth 2.1 with PKCE authentication.

Remote MCP server for managing TickTick tasks and projects, offering 22 tools for CRUD, search, and GTD workflows via any MCP client.

A comprehensive and efficient Model Context Protocol server for task management that works with Claude, Cursor, and other MCP clients, providing powerful search, filtering, and organization capabilities across multiple file formats.

An MCP server that exposes outputty's task tracker as typed tools backed by GitHub Issues, allowing coding agents to manage tasks with two-way sync.

Enables users to manage a persistent todo list through natural language, supporting adding, listing, toggling completion status, and removing todos. Features both stdio and HTTP transports with optional authentication and JSON file storage.

Enables interacting with YouTube Data API v3 through MCP tools (get-video, get-channel, get-latest-video) and bundled UI apps for video and channel profiles.

This server provides a Model Context Protocol interface for a local task management service. It enables AI assistants to manage tasks, organize projects, and view daily schedules through a robust set of provided tools.

Enables LLM clients such as Cursor and Claude to discover and use generated REST services through MCP tools for create, update, and delete actions and MCP resources for list and get-by-id context. Services generated in one command are exposed simultaneously as REST endpoints and MCP tools and resources.

Enables managing tasks in a structured TODO.md file through MCP tools for listing, adding, updating, setting status, logging notes, searching, and removing tasks while preserving hand-authored formatting.

Enables AI assistants to interact with generated REST APIs by discovering and using CRUD tools and read-only data resources.

Enables AI assistants to discover and invoke CRUD tools and read-only resources over the Model Context Protocol for generated API services.

MCP server for agentic task orchestration and management, enabling AI agents to create goals, plan tasks with acceptance criteria, track dependencies, request human input, and provide proof of completion.

MCP server for TickTick API enabling task management, project organization, habit tracking, and more.

MCP server for integrating TickTick task management with AI applications, enabling task and project operations via natural language.

Enables AI assistants to interact directly with generated REST APIs by exposing CRUD operations as MCP tools and read-only data as resources.

Exposes generated CRUD services to LLM clients over the Model Context Protocol, providing tools for create, update, and delete actions plus read-only resources for listing and fetching records by id. It lets assistants like Claude, ChatGPT, and Cursor discover and manipulate the same data served by the project's REST endpoints without any extra glue code.

Exposes every generated CRUD service to LLM clients such as Claude, ChatGPT, or Cursor through discoverable MCP tools for create, update, and list actions plus read-only resources, so models can query and manipulate data without raw HTTP calls. It runs alongside the same codebase's REST endpoints, letting clients and AI share one data layer.