Think inside your AI world.

Miro through Unl

Miro shows the board and every sticky note on it. Whether the workshop actually landed a decision is your call - a full board isn't the same as a resolved one.

Miro through Unl reads boards, items and comments against the decision criterion you have ratified, so 'did this workshop produce an outcome' comes back good-or-flag with the condition it hasn't met.

What Miro holds

Miro's official MCP server exposes:

  • board_search_boards - search and list boards accessible to the current user
  • board_list_items - list items on a board, filterable by type or parent container
  • context_get - get text context from a board or a specific item
  • comment_list_comments - list comments from a board or a specific item

What the naked read gives you

Ask Miro's own MCP what came out of Tuesday's roadmap workshop and it lists everything on the board: forty-three sticky notes, five clusters, a dozen open comment threads. That's a complete and current read of the canvas. What it doesn't say is whether any of those clusters got turned into an actual next step, or whether the board is just forty-three ideas nobody chose between. Miro captures everything on the canvas faithfully, but turning a cluster of stickies into a decision is a judgement the canvas doesn't make.

The frame judges the data it is given; it does not verify the source’s accuracy.

What changes when Miro is read measured

Your design lead ratified the rule in Unl: a workshop only counts as having produced an outcome once a cluster is tagged as the chosen option and a follow-up owner is named in a comment - because a full board with no chosen cluster is just a well-organised brainstorm.

'Did Tuesday's roadmap workshop produce an outcome' returns, measured: not met - five clusters exist, but none is tagged chosen and no owner is named in the comments. Miro supplied the board items and comment threads; your ratified rule supplied the decision criterion.

And back again

The decision is ratified into Unl, so 'decided' for this workshop carries a fixed meaning and the next board in the series reads its clusters against the tagged-and-owned bar instead of counting stickies as progress.

The answer comes back measured against what you already decided, and why.

The lane is live and open to this tool today: one box, paste anything. If it speaks MCP, Unl can reach it. Readings arrive unprompted, the data beside the criterion; Unl is a courier, not a warehouse, and keeps only your keys and the frame.

Questions people ask

Can Miro tell me if a workshop actually produced a decision?

Miro's MCP returns the board's items and comment threads; it doesn't decide when a cluster of ideas counts as a decision. That's the criterion you ratified in Unl, which turns the board read into a verdict.

How do I connect Miro to Claude?

Miro runs an official MCP server built in collaboration with Anthropic, using OAuth 2.1 with dynamic client registration - run the auth flow once and Claude, Claude Code or another compatible client connects directly.

Does connecting Miro let Unl edit my boards?

No - Unl reads the boards, items and comments Miro's MCP exposes. It doesn't move stickies or add content to your boards on your behalf.

What this is

Think inside your AI world — you stay in command

Unlimitless (Unl to friends) holds what you've settled, reads what your tools are showing, and catches what's changed out in the world — and hands your AI whatever bears on the work, the moment it's needed, without you asking. The right thing, in front of the model, unprompted, with you in command of the call. So you keep moving toward what you set out to build, on top of everything you've already decided.

It plugs into Claude, Claude Code, ChatGPT and Cursor as an MCP connector. Quick to connect, in a couple of steps.

Unlimitless is open now to invited Alpha. Apply for the Beta waitlist to come in ahead of the full launch:

Alpha is invite-only · free at launch.