Your AI coding assistant can now read your Miro boards. • Code → Visual Docs: Open your AI tool in your repo, ask "Document this on Miro" - get architecture diagrams, flowcharts, data models. • Board Context → Code: AI reads your PRDs, specs, diagrams, flows, prototypes, decisions and generates code matching your plans. • Works with: Cursor, Claude Code, Windsurf, VSCode + Copilot, Replit. • 2-min setup, OAuth. Public beta.
Hi PH! I'm Łukasz, Director of Engineering at Miro. Super excited to share what we've been building.
The backstory: As you can imagine, our engineering team lives in Miro - we have PRDs, diagrams, user flows, wireframes, technical specs, user insights... everything.
But when we used AI coding tools like Cursor or Claude Code, they had zero context about any of this. We'd spend forever exporting, screenshoting and re-explaining everything that already existed on our boards.
Frustrating! So we built Miro MCP Server
Now AI coding tools can read Miro boards directly. Point AI at your Miro board with PRDs, prototypes, notes and it can read everything and build exactly what you mean.
But even better, you can use Miro visual capabilities to help you understand your codebase. Imagine this:
You're looking at an unfamiliar codebase. Instead of reading files for hours, you ask your AI tool: "Document this entire system on Miro." Five minutes later, you have architecture diagrams showing how everything connects. People are using it for onboarding new engineers (visualize the codebase before diving in), for keeping documentation current (auto-generate diagrams), and for finally getting AI to stop hallucinating when building features.
What I'd love to hear: We're in public beta specifically to learn what we're missing.
What use cases matter to you?
What's broken?
What should we build next?
I'm here all day to answer questions, help with setup, or just chat about where AI + visual context is heading. Let's go!
Congrats on the launch! Giving AI coding tools direct access to visual product context feels like a big unlock, especially for reducing re-explanation and hallucinations. How do you think about keeping Miro boards and the underlying codebase in sync over time, so the diagrams and PRDs stay a reliable source of truth as systems evolve?
Board context → code is the direction I keep thinking about. But it raises a question I don't have a good answer for: once an agent can read your PRD board, who decides if it can also edit it? Or read board A but not board B?
MCP is great for connectivity. The permissions side feels underspecified though. We're working on this problem at keypost.ai - basically a policy layer for MCP servers.
Have you seen teams ask for this kind of control yet, or is everyone still in "let it read everything" mode?
Every time I've pointed an AI coding tool at a PRD, I end up copy-pasting sections into the prompt and losing the visual layout. Miro MCP pulling board context directly into Cursor or Claude Code skips that whole translation step. Biggest question is how it handles large boards with mixed content... rate limits plus noisy context could get tricky fast.
About Miro MCP on Product Hunt
“Turn code into visual docs, board context into code”
Miro MCP launched on Product Hunt on February 5th, 2026 and earned 142 upvotes and 14 comments, placing #9 on the daily leaderboard. Your AI coding assistant can now read your Miro boards. • Code → Visual Docs: Open your AI tool in your repo, ask "Document this on Miro" - get architecture diagrams, flowcharts, data models. • Board Context → Code: AI reads your PRDs, specs, diagrams, flows, prototypes, decisions and generates code matching your plans. • Works with: Cursor, Claude Code, Windsurf, VSCode + Copilot, Replit. • 2-min setup, OAuth. Public beta.
Miro MCP was featured in Software Engineering (42.3k followers), Developer Tools (511.2k followers) and Artificial Intelligence (466.5k followers) on Product Hunt. Together, these topics include over 159.8k products, making this a competitive space to launch in.
Who hunted Miro MCP?
Miro MCP was hunted by Łukasz Sągol. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Miro MCP stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Łukasz Sągol
Hi PH! I'm Łukasz, Director of Engineering at Miro. Super excited to share what we've been building.
The backstory:
As you can imagine, our engineering team lives in Miro - we have PRDs, diagrams, user flows, wireframes, technical specs, user insights... everything.
But when we used AI coding tools like Cursor or Claude Code, they had zero context about any of this. We'd spend forever exporting, screenshoting and re-explaining everything that already existed on our boards.
Frustrating!
So we built Miro MCP Server
Now AI coding tools can read Miro boards directly. Point AI at your Miro board with PRDs, prototypes, notes and it can read everything and build exactly what you mean.
But even better, you can use Miro visual capabilities to help you understand your codebase. Imagine this:
You're looking at an unfamiliar codebase. Instead of reading files for hours, you ask your AI tool: "Document this entire system on Miro." Five minutes later, you have architecture diagrams showing how everything connects. People are using it for onboarding new engineers (visualize the codebase before diving in), for keeping documentation current (auto-generate diagrams), and for finally getting AI to stop hallucinating when building features.
What I'd love to hear:
We're in public beta specifically to learn what we're missing.
What use cases matter to you?
What's broken?
What should we build next?
I'm here all day to answer questions, help with setup, or just chat about where AI + visual context is heading. Let's go!