About Berrys AI
Berrys AI is a remote MCP server for creating product listing images, campaign assets, and short videos on a visual canvas. Agents plan work while users review edits and approve generation before credits are spent.
Key features
- 01Generate listing image sets from a product photo
- 02Plan campaigns and create coordinated images and copy
- 03Create multi-shot product videos
- 04Review, edit, undo, or restore on a shared canvas
Use cases
- 01Prepare storefront hero, lifestyle, and detail images
- 02Create campaign assets from a brief
- 03Storyboard short product videos with consistent products
MCP support
The remote Streamable HTTP server connects AI agents to Berrys through OAuth. It exposes canvas-based image, copy, and video generation workflows with user review and approval.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://app.berrys.io/api/mcpclaude mcp add --transport http generalberrys-berrys-canvas https://app.berrys.io/api/mcp{"mcpServers":{"generalberrys-berrys-canvas":{"type":"http","url":"https://app.berrys.io/api/mcp"}}}Open in Cursor{"servers":{"generalberrys-berrys-canvas":{"type":"http","url":"https://app.berrys.io/api/mcp"}}}Open in VS Code{"mcpServers":{"generalberrys-berrys-canvas":{"type":"http","url":"https://app.berrys.io/api/mcp"}}}{"mcpServers":{"generalberrys-berrys-canvas":{"serverUrl":"https://app.berrys.io/api/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEBerrys AI — MCP Server
Your agent turns product photos into on-brand listing images, ads, and short videos on one canvas.
Berrys connects to the AI agent you already use: Claude, Claude Code, ChatGPT, Codex, Cursor, Gemini CLI, or VS Code. You plan the work with your agent. Berrys builds it on your visual canvas and runs the generation, with your credits and your approval.
What it does
- Listing production — one product photo becomes hero, lifestyle, and detail shots for your storefront.
- Campaign rollouts — hand over a brief; your agent plans the shot list, generates coordinated image sets and copy variants on a shared canvas.
- Short product videos — storyboard a multi-shot clip and keep the product identical across cuts: the last frame of one shot seeds the next.
- Live review — the agent shares a canvas link after each step so you can edit, undo, or restore.
Connect
The server is remote (Streamable HTTP + OAuth). There are no keys to copy — you sign in to Berrys in the browser on first connect.
Server URL: https://app.berrys.io/api/mcp
| Client | How to add |
|---|---|
| Claude (web/desktop) | Settings → Connectors → Add custom connector → paste the URL |
| Claude Code | claude mcp add --transport http berrys https://app.berrys.io/api/mcp |
| ChatGPT | Settings → Apps & Connectors → Advanced → Developer mode → Create connector with the URL |
| Codex | codex mcp add berrys --url https://app.berrys.io/api/mcp then codex mcp login berrys |
| Cursor | Add .mcp.json from this repo (or paste the URL in MCP settings) |
| VS Code | Add the mcpServers entry from .mcp.json |
| Gemini CLI | gemini mcp add --transport http berrys https://app.berrys.io/api/mcp |
First check (free): ask your agent "List my Berrys projects."
Repo layout
| Path | Purpose |
|---|---|
.mcp.json | MCP server descriptor (auto-detected by Cursor Directory and Open Plugins clients) |
skills/berrys/SKILL.md | Agent skill — how agents should drive Berrys (quotes, approvals, canvas workflows) |
rules/berrys.mdc | Cursor rule for a consistent agent experience |
Spending credits
Building and browsing are free. Only generation spends Berrys credits. The agent shows an itemized quote first and waits for your explicit confirmation before submitting a charge.
Documentation
- Setup guides for every client: berrys.io/plugins
- User guide: app.berrys.io
- Registry listing:
io.github.GeneralBerrys/berrys-canvason the Official MCP Registry
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.