Cross-Agent Tasks
pf-tasksThe cross-agent to-do system.
What it does
The cross-agent to-do system. It wraps Google Tasks so agents can hand work to one another and track it to completion on the right, registered task list.
Where the work lands
Each agent gets its own list. This is the shared Google Tasks board the connector reads and writes — one column per agent, work handed off between them.
Tools it exposes
Connects to Google Tasks. Each tool below is a single, well-defined capability — described in plain language.
- List_Task_ListsSee the available task lists (a verification helper).Connects to: Google Tasks
- Get_TaskRead a single task by its ID.Connects to: Google Tasks
- Create_TaskAdd a task with a title, notes, status, and due date.Connects to: Google Tasks
- List_TasksBrowse tasks in a list, with optional filters.Connects to: Google Tasks
- Update_TaskChange a task's title, notes, status, or due date.Connects to: Google Tasks
Built and maintained by the MCP agent
This server was built with FastMCP and Python by our MCP agent, to spec. In practice it takes well under thirty minutes of hands-on direction — agents are genuinely good at building these. The value is in the design: each server exposes only the endpoints we want, with clear descriptions and parameters tuned to our goals.
You don't need all of this to start
Most of what these agents rely on has a simpler equivalent. Anywhere we use a database, you can usually start with a spreadsheet or a document — whatever you're comfortable with. Anywhere we built a custom MCP server, you can reach for a prebuilt AI connector instead of building your own.
We go further because it's where our experience pays off. Years of hands-on database work make a custom data layer feasible for us, and purpose-built MCP servers let us hand each agent a short, sharp instruction file and exactly the tools it needs — custom descriptions, only the endpoints we want, and the occasional extra gate — instead of a long manifesto or playbook. Nothing more, nothing less.
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