Honcho
Memory that reasons: continual learning for stateful agents. Better context, fewer tokens.
作者提供的说明(英文原文)
An AI 3D workspace for games, VR, and beyond. Generate, remesh, texture, and animate 3D assets.
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
通过 3/3 项检查 · 在你的电脑上运行
第一步:你用哪个 AI?
选择你用的应用,步骤会显示在下方。
可连接 Claude,但需要先准备
需要 PicoBerry 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用
SettingsDeveloperEdit Config
{
"mcpServers": {
"picoberry": {
"command": "npx",
"args": [
"-y",
"@picoberry/mcp-server"
],
"env": {
"PICOBERRY_API_KEY": "your-value",
"PICOBERRY_API_BASE": "your-value"
}
}
}
}只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人
用 PicoBerry 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude 说正在使用 PicoBerry,或回答中出现 PicoBerry 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
ChatGPT 暂时无法连接 PicoBerry
PicoBerry 需要安装在你的电脑上,而 ChatGPT 只能连接在网上运行的服务器
A · 使用能连接 PicoBerry 的应用
B · 游戏、体育、美食与学习 类中能连接 ChatGPT 的其他服务器
从同类中使用较多的里挑选,仅限用地址即可连接、无需密钥的
可连接 Claude Code,但需要先准备
需要 PicoBerry 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次)
Mac:按 Command+空格,输入 Terminal · Windows:打开 PowerShell
claude mcp add picoberry --env PICOBERRY_API_KEY=your-value --env PICOBERRY_API_BASE=your-value -- npx -y @picoberry/mcp-server按 Enter 前,把“your-value”改成你的密钥
只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人
应能看到 PicoBerry 在列表中并显示已连接
用 PicoBerry 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude Code 说正在使用 PicoBerry,或回答中出现 PicoBerry 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
可连接 Cursor,但需要先准备
需要 PicoBerry 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次)
在 PicoBerry 的设置中,PICOBERRY_API_KEY, PICOBERRY_API_BASE 还是空的,填入你的密钥并保存
只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人
用 PicoBerry 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Cursor 说正在使用 PicoBerry,或回答中出现 PicoBerry 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Cursor 帮助页面核实 · 你的界面可能因版本略有不同
可连接 VS Code,但需要先准备
需要 PicoBerry 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次)
在 PicoBerry 的设置中,PICOBERRY_API_KEY, PICOBERRY_API_BASE 还是空的,填入你的密钥并保存
只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人
用 PicoBerry 告诉我你能帮我做什么,然后做一件给我看✓ 如果 VS Code 说正在使用 PicoBerry,或回答中出现 PicoBerry 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 VS Code 帮助页面核实 · 你的界面可能因版本略有不同
Claude Code
claude mcp add picoberry --env PICOBERRY_API_KEY=your-value --env PICOBERRY_API_BASE=your-value -- npx -y @picoberry/mcp-serverClaude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"picoberry": {
"command": "npx",
"args": [
"-y",
"@picoberry/mcp-server"
],
"env": {
"PICOBERRY_API_KEY": "your-value",
"PICOBERRY_API_BASE": "your-value"
}
}
}
}Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"picoberry": {
"command": "npx",
"args": [
"-y",
"@picoberry/mcp-server"
],
"env": {
"PICOBERRY_API_KEY": "your-value",
"PICOBERRY_API_BASE": "your-value"
}
}
}
}VS Code (mcp.json)
{
"servers": {
"picoberry": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@picoberry/mcp-server"
],
"env": {
"PICOBERRY_API_KEY": "your-value",
"PICOBERRY_API_BASE": "your-value"
}
}
}
}PICOBERRY_API_KEY 机密 必填 envPICOBERRY_API_BASE env这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
依据这个服务器的连接方式和各应用自己的文档,检查于 2026年11月10日。
在你的电脑上运行,适用于
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月12日
注册名称已通过 GitHub 账户 UModeler 验证,该账户拥有此仓库
代码最后更新于8天前
使用 MIT
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
提问、分享技巧或报告无法使用。按“有帮助”数量排序。
还没有评价
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 3 项适用的基础检查中通过了 3 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
你需要填写:PICOBERRY_API_KEY
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于8天前,最新版本(0.1.7)于21天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Honcho, ShipSwift, Pm Claude Skills
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Memory that reasons: continual learning for stateful agents. Better context, fewer tokens.
1310 professional Agent Skills + workflow recipes — searchable & fetchable over MCP.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
PicoBerry MCP Server
Generate 3D models, images, and animations for game and 3D workflows 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. Generated assets are drafts — useful for prototyping and iteration, and can be reviewed or refined for your project.
📖 Full reference: API + MCP docs · PicoBerry API
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.)
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.
Sign in at https://picoberry.ai, open the [API Keys](https://picoberry.ai/dashboard/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.)
| Var | Required | Default | Notes |
|---|---|---|---|
PICOBERRY_API_KEY | ✅ | — | pb_live_... |
PICOBERRY_API_BASE | — | https://api.picoberry.ai | leave unset unless you were given a different host |
| Tool | What it does |
|---|---|
list_models | Engines + credit cost for a category (3d / image / parts-board / remesh / texture / animate). Call before generating — don't hardcode engines. |
list_animation_presets | Animation preset ids, per engine family (tripo / meshy / pb), with optional substring filter. pb is PB Animation's motion list; each item carries body. |
get_credits | Current credit balance. |
generate_image | Text → image (+ optional reference image URLs). count (1–4) makes several images in one call → assets[], wait on each id. |
generate_3d_from_text | Text → 3D model (GLB). |
generate_3d_from_image | Image → 3D model. Single: image_url or local image_path. Multi-view (2–4 views, higher fidelity): image_urls or image_paths, ordered [front, left, back, right] — tripo\*/meshy6/hunyuan-3.x only. |
parts_board | Decompose one image into an exploded parts-board image (server-fixed engine). Input asset_id, image_url, or local image_path; feed the result to generate_3d_from_image for a parts-separated mesh. |
remesh | Retopologize an existing 3D asset → new asset. |
texture | Re-texture (PBR) an existing 3D asset → new asset. |
animate | Auto-rig (+ optionally animate) an existing 3D asset → new asset. Tripo/Meshy rig humanoids; PB Animation (pb-rig + body: humanoid / quadruped / other) also rigs four-legged and other characters — quadrupeds are rig-only for now. |
get_asset | Status + result URLs for one asset. |
wait_for_asset | Poll until an asset finishes (or times out), then return it. |
list_my_assets | Browse your generated assets. |
download_asset | Export a completed 3D asset (glb / fbx / obj) → signed URL. |
Generation is asynchronous:
generate_3d_from_text({ prompt }) → returns an asset { id }.wait_for_asset({ asset_id: id }) → polls until taskStatus === 2 (succeeded).files.model (GLB) or files.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).
"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") → { 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 URL
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"] } } }
npm install
npm run build # tsc → dist/ PICOBERRY_API_KEY=pb_live_... npm start
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 | UModeler |
| Repository | picoberry-mcp |
| Workflow filename | publish.yml |
| Environment name | *(leave empty)* |
| Allowed actions | npm publish |
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. /* 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-version
pin 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 lowercased
io.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.
MIT