What is MCP (Model Context Protocol), and why it matters for your tasks

MCP — the Model Context Protocol — is an open standard for connecting AI agents to external tools and data. Think of it as a universal port: instead of every app building a one-off integration for every AI model, a tool exposes an MCP endpoint once, and any MCP-compatible client — Claude, ChatGPT connectors, Cursor, or a custom agent — can connect to it.

Why does MCP matter?

Before MCP, giving an AI access to your tools meant bespoke plugins, per-model. MCP replaces that with one standard interface, which means two things for you: your choice of AI model isn't locked to one app, and tools can expose real, safe actions rather than just text.

• Model-agnostic: connect whichever agent you prefer today, and switch tomorrow without changing tools.
• Action-oriented: an MCP server exposes named tools (create a task, claim it, comment) instead of a chat box.
• Scoped: access is granted per connection, so an agent only ever sees what you allow.

What is an MCP-native task manager?

It's a task manager that exposes your tasks over MCP so agents can operate on them directly. In Done, every account gets its own remote MCP endpoint. Connect an agent over OAuth or a bearer token and it can pull its queue, claim a task, do the work, and post results — all scoped to your account alone, with no shared or global token.

Is MCP secure?

The protocol itself is a transport; security comes from how a server implements it. A good MCP task manager authenticates per account, lets you scope tokens down to read-only or a single project, and keeps a human in the loop for consequential actions. That's how you get the benefit of agent access without handing over the keys.