PicoBerry MCP Server
The PicoBerry MCP Server lets you generate game-ready 3D models, images, and animations from any MCP client (Claude Code, Cursor, Claude Desktop, Cline) using PicoBerry credits — no separate subscription required.
Discovery & Account
list_models– Browse available engines and credit costs by category (3D, image, remesh, texture, animate)list_animation_presets– Discover engine-specific animation preset IDs (Tripo, Meshy) with optional keyword filteringget_credits– Check current credit balance and plan details
Generation
generate_image– Create 2D images from text prompts with optional reference image URLs and aspect ratio controlgenerate_3d_from_text– Turn a text prompt into a game-ready GLB model with optional texture and polycount settingsgenerate_3d_from_image– Convert a single image (URL or local file path) into a 3D GLB model
Post-Processing
remesh– Retopologize an existing 3D asset to a new polycounttexture– Re-texture an existing 3D asset with PBR materials using a text promptanimate– Auto-rig and animate a 3D character asset using animation presets
Asset Management
get_asset– Retrieve status and result URLs (model, image, thumbnail) for any assetwait_for_asset– Poll until an asset completes or times out (configurable interval)list_my_assets– Browse previously generated assets with filtering by category or keyworddownload_asset– Export completed assets as GLB, FBX, or OBJ (with optional Unity texture preset)
It can also be used alongside Blender MCP — generate assets with PicoBerry, then import directly into Blender.
Allows generating 3D models with PicoBerry and importing them into Blender in one workflow.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@PicoBerry MCP Servergenerate a low-poly treasure chest"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Generate game-ready 3D models, images, and animations from any MCP client —
Claude Code, Cursor, Claude Desktop, Cline — with no HTTP glue. A thin wrapper
over the PicoBerry /v1 API, so you get PicoBerry's
multi-engine pipeline directly inside your agent. Several 3D and image engines
sit behind one API; call list_models for the live set and each engine's cost.
There's no separate subscription for the MCP or the API. Generation spends the same prepaid PicoBerry credits as the web app, per engine, at rates you can read with
list_modelsbefore you spend anything. (Using the API does require a completed purchase — see Get an API key.)
Install
No install needed — run it with npx:
// Claude Code: .mcp.json · Claude Desktop: claude_desktop_config.json
{
"mcpServers": {
"picoberry": {
"command": "npx",
"args": ["-y", "@picoberry/mcp-server"],
"env": {
"PICOBERRY_API_KEY": "pb_live_xxxxxxxxxxxxxxxx"
}
}
}
}Cursor uses the same shape in ~/.cursor/mcp.json.
Get an API key
Sign in at https://picoberry.ai, open the API Keys tab in your dashboard, and hit Create key. The key is shown once — copy it immediately and treat it like a password.
API access needs a completed purchase: a subscription or a one-off credit pack. A purchase entitles you permanently — you don't need a current subscription. (An active paid subscription works too, of course.)
Environment variables
Var | Required | Default | Notes |
| ✅ | — |
|
| — |
| leave unset unless you were given a different host |
Related MCP server: higgsfield-mcp
Tools
Tool | What it does |
| Engines + credit cost for a category ( |
| Animation preset ids (engine-specific), with optional substring filter. |
| Current credit balance + plan. |
| Text → image (+ optional reference image URLs). |
| Text → 3D model (GLB). |
| Image → 3D model. Single: |
| Retopologize an existing 3D asset → new asset. |
| Re-texture (PBR) an existing 3D asset → new asset. |
| Auto-rig + animate an existing 3D character → new asset. |
| Status + result URLs for one asset. |
| Poll until an asset finishes (or times out), then return it. |
| Browse your generated assets. |
| Export a completed 3D asset ( |
How generation works
Generation is asynchronous:
generate_3d_from_text({ prompt })→ returns an asset{ id }.wait_for_asset({ asset_id: id })→ polls untiltaskStatus === 2(succeeded).Read the result URL from
files.model(GLB) orfiles.image(PNG).
taskStatus: 0 pending · 1 processing · 2 succeeded · 3 failed. Result
URLs are signed and short-lived — download promptly. Errors come back with an
actionable message (e.g. an unknown engine returns the list of valid names).
Example (in an agent)
"Make a low-poly treasure chest, retopo it to 3k tris, and give me a Unity FBX."
list_models(category="3d") → pick an engine
generate_3d_from_text(prompt="low-poly treasure chest, game ready") → { id: A }
wait_for_asset(asset_id=A) → taskStatus 2
remesh(asset_id=A, polycount=3000) → { id: B }
wait_for_asset(asset_id=B)
download_asset(asset_id=B, format="fbx", texture_preset="unity") → signed URLUse it alongside Blender MCP
Run this next to blender-mcp and the
agent can generate with PicoBerry, then import into Blender in one flow:
{
"mcpServers": {
"picoberry": { "command": "npx", "args": ["-y", "@picoberry/mcp-server"], "env": { "PICOBERRY_API_KEY": "pb_live_..." } },
"blender": { "command": "uvx", "args": ["blender-mcp"] }
}
}Develop
npm install
npm run build # tsc → dist/
PICOBERRY_API_KEY=pb_live_... npm startRelease
Run Actions → Publish → Run workflow (or push a v* tag). It publishes to
npm and then to the official MCP registry, in that order — the registry
validates by fetching the package's npm metadata and matching its mcpName
against server.json's name, so npm has to land first. A guard step checks
every invariant (name/version agreement, namespace casing, version not already
on npm) before anything is published, because npm versions are immutable and a
failed half-publish burns the number.
Bump version in both package.json and server.json (version and
packages[0].version) — the guard fails the run if they disagree.
One-time setup — no secrets. Both publishes authenticate over the workflow's
GitHub OIDC token (id-token: write). There is nothing to store or rotate.
The only step is telling npm to trust this workflow. On npmjs.com go to @picoberry/mcp-server → Settings → Trusted publishing → GitHub Actions and enter:
Field | Value |
Organization or user |
|
Repository |
|
Workflow filename |
|
Environment name | (leave empty) |
Allowed actions |
|
The workflow filename must match exactly — it is part of what npm verifies.
The MCP registry needs no setup at all: mcp-publisher exchanges the Actions
OIDC token, and the registry grants io.github.<repository_owner>/* from the
token's repository_owner claim. That covers io.github.UModeler/picoberry-mcp
and avoids the interactive browser login (which additionally requires org Owner).
Trusted Publishing needs npm >= 11.5.1, so the workflow runs on Node 24 (npm 11.x). Node 22 still bundles npm 10.9 and would fail — the
node-versionpin is load-bearing. A guard step fails the run early if the runner ever ships an older npm.
The namespace is compared byte-exactly —
io.github.UModeler/..., matching the GitHub org's login. A lowercasedio.github.umodeler/...is rejected 403.
After publishing, claim the Glama listing
— unclaimed servers get limited discoverability, and awesome-mcp-servers gates
its PRs on a Glama badge in CI.
License
MIT
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- Alicense-qualityCmaintenanceAI-powered image generation MCP server with 16 specialized tools for generating, editing, analyzing, and processing images using Google's Nano Banana 2 model. Supports custom API endpoints and integrates with AI coding assistants via natural language.Last updated3941MIT
- Alicense-qualityCmaintenanceEnables AI-powered image and video generation using Higgsfield AI models through MCP-compatible clients like Claude Desktop and Perplexity.Last updated282MIT
- Alicense-qualityBmaintenanceEnables generating 3D models from text prompts, rendering turntable animations, and uploading them directly to YouTube via an MCP interface.Last updated12MIT

Context3D MCP Serverofficial
AlicenseBqualityDmaintenanceEnables AI-powered 3D model generation from text and images with PBR textures, supporting blockchain authentication and MCP integration.Last updated720MIT
Related MCP Connectors
MCP server for NanoBanana AI image generation and editing
Generate authentic pixel art - sprites, animations, and tilesets - from any MCP client
MCP server for Google Veo AI video generation
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/UModeler/picoberry-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server