The Model Context Protocol (MCP) lets an AI assistant such as ChatGPT or Claude call tools on your server. Several closet apps already have an MCP server: the assistant reads your clothes and tells you what to wear.
We compared 10 wardrobe MCP servers on 13 features, from their listings, tool lists and setup guides. The comparison itself is for shoppers. But the servers make very different design choices, and those choices apply to any app with personal data: notes, recipes, a photo library, a budget. Here are 6 of them.
1. Decide how much the assistant may change
The servers sit on a scale from read-only to full control.
| Server | What the assistant may do with pieces |
|---|---|
| Fits | Read pieces and outfits. It cannot add, edit or delete pieces. |
| Aesty | Read, add from a photo or a shop link, remove. |
| Caffeinated Wardrobe | Create, read, change and delete items and outfits. |
Read-only is safe and easy to explain. Full control turns the server into an API for the user's data, but every delete tool needs a clear description so the model does not call it by mistake. Pick the level on purpose, and write it in the server description.
2. Decide who does the thinking
Some servers bring their own stylist: Aesty asks its own stylist for outfits from the user's style profile, and Vêtir has a stylist that builds looks from the closet and its shop. Caffeinated Wardrobe has no stylist at all. Its listing says the AI you connect does the styling.
Both work. The question is where your value is. If it is your model or your rules, expose them as a tool ("suggest_outfit"). If it is the data, expose clean data and let the assistant reason.
3. More tools is not more features
| Server | Tools | Features (of 13) |
|---|---|---|
| Vêtir | 24 | 8 |
| Caffeinated Wardrobe | 20 | 5 |
| Aesty | 17 | 9 |
| Fits | 14 | 5 |
| OutfitMaker | 6 | 4 |
| My Closet | 4 | 3 |
Aesty does more than Vêtir with 7 fewer tools. Every tool adds text to the model's context and one more choice to get wrong. Merge tools that differ only by a filter.
4. Offer something without sign-in
OutfitMaker has two MCP addresses. One works without an account and gives outfit advice for a city's weather and an occasion. The other reads the user's closet. Vêtir lets anyone search its shop, and asks for sign-in only for the closet.
An anonymous tool lets a person try the server in one minute. Sign-in can come later, when they want their own data.
5. Return pictures, not only JSON
Clothes are visual. Fits returns grid and collage cards of the user's pieces. OutfitMaker draws the outfit on a model as a picture in the chat. Modebase shows outfits as flat-lay pictures.
If your data has images, return them in a form the chat can show. A list of IDs and colour names is correct, but it is hard to read in a chat.
6. Get the data in without manual work
The hardest part of a closet app is the first 100 pieces. The servers attack it from different sides: add from a photo, add from a shop's product link (Aesty), or build the closet from purchase emails (Closai). An MCP tool that imports in bulk is worth more than ten tools that edit one item.
Summary
- Choose the write scope on purpose.
- Decide if your server thinks or only serves data.
- Keep the tool list short.
- Give one useful tool without sign-in.
- Return images when your data is visual.
- Make the first import easy.
The full comparison of the 10 servers, with what each one exposes and how to connect it, is at digital-wardrobe.app/mcp-servers.
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