A workshop board can contain the reasoning behind a project while an AI assistant sees only a short prompt. Miro’s October 7 report draws attention to that gap through its MCP server, which lets supported AI tools read board context and add content to the canvas.
The server launched in February; this is an adoption update, not a new launch. Miro reports more than 19 million tool calls since then and says three quarters of its MCP users work outside engineering. Those are Miro’s own figures, not an independent measure of useful work or output quality.
Bring the decision trail into view
Consider turning a discovery workshop into a proposed customer journey. The board might contain competing ideas, rejected options, and unanswered questions. Access to that material could give an assistant more useful context than a polished summary that quietly loses the uncertainty.
Our suggested first check is whether the resulting artifact preserves that uncertainty. Can a teammate distinguish agreed decisions from suggestions? Are the relevant frames and source notes easy to find? Start with a clearly bounded board area and review what the agent reads and changes. Connecting an assistant to the canvas helps move information; the team still owns the interpretation of what it means.
