Open supply initiatives normally start with a easy perception: software program turns into extra succesful, extra resilient, and extra significant when it’s created within the open. For Penpot, this perception is just not a slogan. It’s the basis of your entire product. Openness guides how options are imagined, how selections are reached, how the group participates, and extra just lately, how Penpot approaches AI.
As AI continues to reshape inventive work, many design instruments have launched clever options developed behind closed doorways. Penpot is selecting one other manner. As an alternative of presenting a completed thought of what AI ought to do, the group invitations the group to discover what AI might do. A lot of what makes a design significant isn’t written straight right into a file.
As Àlvaro Tejero famous at Penpot Fest, a design carries intent and relationships that aren’t recorded within the code, but designers acknowledge them immediately.
The brand new MCP Server sits on the coronary heart of this mindset. It isn’t a product announcement however an invite to discover collectively what AI assisted design might turn out to be.
Why Open Collaboration Issues for Design Instruments
The design business strikes shortly. New practices and applied sciences seem at a gradual tempo, and the boundary between design and growth turns into thinner yearly. On this surroundings, a instrument formed by a small closed group can start to really feel inflexible or disconnected from actual every day work.
Penpot follows a special philosophy. Openness is just not solely about releasing supply code. It’s about creating room for significant contribution. Clear processes and extensible instruments forestall concepts from getting trapped inside a single firm. As an alternative, concepts enhance by means of actual world suggestions and experimentation. That is very true in design, the place which means comes from context, emotion, and intent as a lot as from seen components. As Tejero noticed, folks naturally perceive extra from a design than what’s written in information or coordinates.
This surroundings permits contributors to affect the route of the product slightly than solely requesting options. That stage of collaboration is troublesome to match in proprietary ecosystems, and it turns into much more worthwhile when AI enters the image. The tempo of AI growth is simply too quick for a closed roadmap to anticipate the whole lot. A group can think about much more.
How Neighborhood Collaboration Shapes Penpot
Anybody who has adopted Penpot in recent times has seen the group form the instrument in seen methods. Massive options equivalent to Design Tokens and Variants have been influenced and sometimes stress examined by designers and builders lengthy earlier than they reached a secure launch.
The beta testing cycles illustrate this properly. Every main characteristic is shared early with the group, who attempt it in actual initiatives, break it in surprising methods, and provide suggestions that helps refine the expertise lengthy earlier than ultimate launch. Choices are made publicly and mentioned with openness, all the time grounded in actual workflows.
The identical collaborative spirit seems within the API ecosystem. By exposing the Plugin API and treating it as a firstclass extension level, Penpot encourages experimentation. Builders have already created workflow automation, integrations with code instruments, accessibility helpers, and early AI brokers that examine designs by means of the plugin layer. These experiments inform the product and assist the group perceive rising wants.
This tradition of transparency and participation makes the subsequent step attainable.
The MCP Server: A New Layer for Co Creation
At Penpot Fest 2025, the group offered an early preview of the MCP Server. It’s a new interface that enables AI brokers and instruments to work together with Penpot designs by means of a versatile and mannequin impartial protocol. As an alternative of counting on screenshots or non-public interfaces, an AI system can use the identical Plugin API out there to people, solely with sooner entry to Penpot’s structured design mannequin.
This opens a door to workflows that weren’t attainable earlier than.
In group demos, AI brokers might analyze design methods, extract type guidelines, refactor naming, generate semantic HTML and CSS, replace kinds from documentation, construct Storybook setups, and even flip tough sketches into extra structured elements.
Penpot constructed the MCP Server round a easy thought: AI turns into extra dependable when it may well perceive specific design construction. As Tejero famous, giant language fashions have limits, however they will work much more precisely after they obtain organized info. By exposing Penpot’s inside graph of relationships as a substitute of uncooked pixels, the server makes semantic understanding attainable.
This preview is deliberately open ended. The MCP Server doesn’t dictate how AI ought to behave. It offers a basis that designers, builders, and toolmakers can discover and adapt. This is the reason it was launched early. As Tejero defined, the purpose was to let folks take a look at it, problem it, and share how they wish to use it.
By sharing the server at this stage, Penpot is actually saying: Here’s a basis. Assist us think about what must be constructed on prime of it.
Why Penpot Is Asking for Use Instances
As a result of AI in design workflows continues to be new, nobody can absolutely predict which concepts will turn out to be important. Some designers need assist cleansing layer buildings. Others need alignment between design and code. Others need refactoring, naming consistency, documentation syncing, or instruments that discover excessive stage concepts.
As an alternative of guessing, Penpot desires to listen to straight from the individuals who will use these instruments. The group already understands the foundational items the MCP Server wants, however they need to study which particular use instances excite the group most. Penpot is reaching the purpose the place the server will evolve from a versatile, generic interface into one thing formed by actual, use-case-driven growth—and group enter is what’s going to information that transition.
These questions are answered finest by means of collaboration with a group that already consists of designers, frontend builders, open supply contributors, educators, and inventive coders.
The MCP Server turns into a catalyst for that collaboration: a versatile basis that may develop in instructions no single roadmap can predict.
Doable Use Instances
Beneath are a few of the early MCP Server experiments proven within the Penpot demos. They illustrate how AI brokers can work straight with Penpot’s structured design mannequin to automate actual duties throughout design and code workflows.
Design to code
An AI agent can learn the construction of a Penpot design — its elements, format, and tokens — and switch that into clear, semantic HTML and CSS. This strikes past easy export: the agent understands the design’s relationships and produces code that displays them.
Prototype interactions
As an alternative of manually wiring up interactions, an AI agent can generate an interactive prototype from the design. It may possibly take current property or HTML information and join them right into a functioning expertise, serving to groups discover habits earlier within the course of.
Scribble to design
A tough sketch can be utilized as the idea for an precise structured format. The agent interprets the scribble’s visible intention and produces a extra refined design that follows the identical basic construction, giving designers a sooner technique to transfer from thought to draft.
Design System documentation + Design tokens
An agent can examine a design or design system, establish token utilization and construction, and generate documentation routinely. This makes it simpler to take care of a shared supply of reality and preserve groups aligned as methods develop.
These experiments barely scratch the floor. They present how the MCP Server turns Penpot right into a playground for automation, creativity, and completely new workflows, the place AI doesn’t change the designer, however removes the friction between creativeness and execution.
Do you see the unimaginable potentialities but?
How You Can Get Concerned
For those who care about the way forward for design, design methods, inventive tooling, or AI assisted workflows, Penpot welcomes your perspective. Whether or not you design advanced interfaces, take pleasure in automation, or experiment with AI brokers, your concepts will help information the expansion of the MCP Server.
You’ll be able to contribute by:
- Sharing your use instances and concepts
- Testing workflows when the beta opens
- Becoming a member of discussions within the Penpot group
- Exploring the Plugin API or early MCP examples
- Providing suggestions on workflows, naming, or design to code processes
To affix the dialog round early experiments and use instances, you possibly can go to the group dialogue house. (You’ll want to enroll in free to entry the thread.)
Even small concepts can affect the route of the venture. A lot of Penpot’s most impactful options started as light-weight experiments or informal conversations throughout the group.
Trying Forward
What makes Penpot particular is just not solely its expertise however the best way it’s constructed, with transparency, care, and a real perception in shared possession. Because the group steps into the world of AI, that philosophy turns into much more vital. As an alternative of releasing a sophisticated assistant, Penpot is creating house for exploration and collective invention.
The MCP Server is simply the start. The way forward for design tooling won’t be outlined by a single firm or mannequin. It will likely be formed by the individuals who use these instruments day-after-day. Penpot is selecting to construct that future brazenly, and if you’re inquisitive about the place AI and design are headed, this is a perfect second to take part.
