Mcp Todo App

Overview

The Mcp Todo App is a lightweight MCP (Model Context Protocol) server that demonstrates how an AI assistant can interact with a simple task‑management data store. It exposes a set of resources and tools that let Claude or other MCP‑compliant clients create, read, update, and delete to-do items in real time. By providing a concrete example of CRUD operations over a shared context, this server helps developers understand how to wire an AI assistant into existing workflows that require persistent state and collaborative editing.

The server’s core value lies in its ability to keep the AI assistant “aware” of user‑generated data. Instead of sending raw text prompts back and forth, the assistant can issue structured tool calls that modify the shared context. For instance, a user might ask the AI to “Add a reminder for the project deadline”, and the assistant will translate that request into a operation on the to‑do resource. Subsequent queries such as “Show me all pending tasks” are resolved by reading the current state of that resource. This pattern eliminates the need for the assistant to maintain its own memory buffer and ensures consistency across multiple clients or sessions.

Key capabilities of the Mcp Todo App include:

• Resource Management: Exposes a endpoint that supports standard HTTP verbs (GET, POST, PUT, DELETE) mapped to MCP tool calls.
• Prompt Templates: Provides pre‑defined prompts that guide the assistant in formatting user requests into tool calls, making it easier to adopt for developers who want a plug‑and‑play integration.
• Sampling and Context Control: Allows fine‑tuned sampling parameters so that the AI’s responses can be adjusted for verbosity or precision when interacting with the to‑do list.
• Real‑time Updates: Uses WebSocket or long‑polling to push changes back to connected clients, ensuring that all participants see the latest state without manual refreshes.

Typical use cases include:

• Personal productivity: Users can manage their daily tasks through conversational AI, letting the assistant add, edit, or prioritize items on demand.
• Team collaboration: Multiple team members can interact with the same to‑do list via a shared chat interface, with the AI coordinating updates and notifications.
• Workflow automation: Integrate the server into larger pipelines where an AI assistant triggers downstream actions (e.g., sending emails or scheduling calendar events) based on the to‑do state.

Integrating this MCP server into an AI workflow is straightforward. A developer first runs the server, then configures their client to point at its endpoint. From there, the assistant can call the exposed tools directly, treating them as first‑class operations rather than text manipulations. This approach reduces hallucinations and ensures that all state changes are auditable, providing a robust foundation for building more complex AI‑driven applications.