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"A service for managing a to-do list and task reminders" matching MCP servers:

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    Local MCP server for A-share stock trading via Tonghuashun, offering account/position queries, buy/sell/cancel orders with risk controls and forced user confirmation; currently simulated with a reserved interface for real broker channels.
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    Enables an AI assistant to manage a simple to-do list stored in a local JSON file, with tools to add, list, get, complete, and delete tasks, plus a read-only pending tasks resource.
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    Enables reading, creating, updating, completing, and deleting local Apple Reminders on macOS through EventKit, with list filtering, due dates, notes, priorities, and an optional read-only mode.
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    Elevate your LLM task management with Task Orchestrator, an MCP server that empowers you to define, organize, and track goals and tasks with hierarchical precision. Integrate intelligent task management into your workflow.
    5
    3 npm
    7
    MIT
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    A Model Context Protocol server that enables AI assistants to interact with Apple Reminders on macOS, allowing users to view lists, retrieve, create, complete, and delete reminders through natural language.
    5
    39
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    Enables users to manage tasks through a simple JSON file interface. Provides basic task management functionality by reading and writing to a configurable tasks.json file.
    14 npm
    MIT
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    Enables AI assistants to manage Dida365 (滴答清单) tasks through the Model Context Protocol. It supports full task lifecycle management including creating, updating, and filtering tasks with automatic timezone handling.
    8 npm
    MIT
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    A local task management MCP server that enables users to create, update, and manage tasks through natural language conversations with Claude. It provides nine tools for comprehensive task management including creation, filtering, searching, and daily planning without requiring a separate UI or backend service.
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  • F
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    A simple MCP server that turns a JSON file into a todo list, letting users add, list, complete, delete, and clear tasks through natural language in MCP-compatible clients.
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