Pascal 3D Editor
Create, inspect, validate, and save editable 3D building scenes with Pascal's MCP server.
Make images and videos, price requests, list AI models and read your balance, from one API key.
选择你使用的 AI 应用,然后复制设置。
通过 4/4 项检查 · 你的提问会经过开发者的服务器(api.uberllm.dev) · ✓ 命令已检查 2026年11月13日
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 · 约 5 分钟
需要 UberLLM 的密钥(在开发者网站创建) · 在 claude.ai 或桌面应用设置一次,即可在聊天中使用 · 免费账户可添加 1 个 · Team/Enterprise 方案由所有者(Owner)添加
CustomizeConnectors
把这里的内容复制到 Claude 中同名的输入框,然后点击 Continue
UberLLMhttps://api.uberllm.dev/mcp在 Request headers 下添加这个名称和值
AuthorizationBearer <在这里粘贴你的密钥>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
在 Claude 检测到的登录设置页点击 Continue · Authentication 按作者说明选择(Sign in now、Sign in when needed 或 No sign in),然后点击 Add
+ConnectorsUberLLM
用 UberLLM 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 UberLLM,或回答中出现 UberLLM 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 UberLLM
UberLLM 连接时需要你自己的密钥,但 OpenAI 帮助页面只说明了 OAuth 登录,没有我们能核实的填密钥方式
A · 使用能连接 UberLLM 的应用
B · 设计、图片与音频 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 · 约 5 分钟
需要 UberLLM 的密钥(在开发者网站创建)
Mac:按 Command+空格,输入 Terminal · Windows:打开 PowerShell
claude mcp add --transport http --scope user uberllm https://api.uberllm.dev/mcp --header 'Authorization: Bearer {api_key}'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 UberLLM 在列表中并显示已连接
用 UberLLM 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 UberLLM,或回答中出现 UberLLM 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 · 约 5 分钟
需要 UberLLM 的密钥(在开发者网站创建)
在 UberLLM 的设置中,Authorization 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 UberLLM 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 UberLLM,或回答中出现 UberLLM 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 · 约 5 分钟
需要 UberLLM 的密钥(在开发者网站创建)
在 UberLLM 的设置中,Authorization 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 UberLLM 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 UberLLM,或回答中出现 UberLLM 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 --transport http --scope user uberllm https://api.uberllm.dev/mcp --header 'Authorization: Bearer {api_key}'Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"uberllm": {
"url": "https://api.uberllm.dev/mcp",
"headers": {
"Authorization": "Bearer {api_key}"
}
}
}
}VS Code (mcp.json)
{
"servers": {
"uberllm": {
"type": "http",
"url": "https://api.uberllm.dev/mcp",
"headers": {
"Authorization": "Bearer {api_key}"
}
}
}
}说明仍为英文原文。
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。
通过互联网连接,适用于
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast
工具名称和描述与服务器在 2026年11月12日 报告的完全一致。这些内容由作者撰写,不是我们写的;我们以纯文本形式显示。
balanceYour balance in US dollars: what you can spend now and what is held for requests and jobs still running.
edit_imageChange a picture you give (image_url or input_images, as https:// links or data: URIs) following a prompt, with an editing model. Spends from your balance.
generate_imageMake pictures from a prompt. Spends from your balance; the answer carries the links and what it cost (usage.cost_usd).
get_jobThe status of a video or music job, its progress and, once it succeeded, the links to its files and what it cost.
get_modelOne model: its price, what it makes and, for an image, video, music or speech model, a JSON Schema of the options a request takes (parameters).
list_modelsModels you can call right now, with their price. Filter by type (chat, image, video, music, speech, transcription) and by words in the id or name.
quoteCheck an image, video, music or text-to-speech request against the model and price it, without making anything or spending. Returns the listed price (cost_usd: it is never billed more) and what is held while it runs (max_cost_usd).
start_videoStart making a video. Returns a job; call wait_job with its id until it is done. Spends from your balance when the video is made; a failed job costs nothing.
wait_jobWait until a video or music job ends, for at most timeout_seconds (50 at most). Returns the job; if done is false, call wait_job again.
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15日
注册名称已通过域名 uberllm.dev 验证(注册中心会核实域名所有权)
代码最后更新于前天
不适用:由 uberllm.dev 的所有者自己运行的云端服务器,你不会运行仓库里的代码
在 347 毫秒内响应(HTTP 200)
无需登录
已读取 9 个工具描述,未发现针对 AI 的隐藏指令
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 4 项适用的基础检查中通过了 4 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:Authorization
它在作者的云端运行。通过互联网连接,无需安装,但你发送的内容会经过作者的服务器。
GitHub 仓库最后更新于前天,最新版本(1.0.0)于16天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,因此也能用于接受远程 MCP 的应用,例如网页版 ChatGPT(Business 及以上为完整 MCP;Pro 为只读)。
同一分类中有:Pascal 3D Editor, Figma MCP Server, Remocn: Remotion MCP server, Harness, ChatCut
同一分类、其他作者、命令已通过检查。官方的排在前面,再按星标排序。
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
Create, inspect, validate, and save editable 3D building scenes with Pascal's MCP server.
The Figma MCP server brings Figma design context directly into your AI workflow.
Hosted MCP for Remotion videos: search Remocn components, read docs, plan a video, preview it.
Start Claude Code and Codex agents the person can watch, wait for them and read their answers.
AI video editor for agents: cut footage, add captions and motion graphics, export editable edits.
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。
UberLLM is an API marketplace for AI models: chat, image, video, music and speech. You call one OpenAI- and Anthropic-compatible API with one key. Each request is served by a third-party inference provider for that model, and costs what that provider charged for it.
https://api.uberllm.dev/v1 (OpenAI) or https://api.uberllm.dev (Anthropic).This repository holds the public tooling: plugins for Claude Code and Codex, a Gemini CLI extension, the uberllm agent skill, the Claude Code installer with its checksums, and copy-paste setup for SDKs and MCP clients.
Sign in at https://uberllm.dev with your email (a code is sent, no password), add credit, and create a key in Dashboard → Keys. Keys start with ull_.
This repository is a Claude Code plugin, a Codex plugin, a Gemini CLI extension and an Agent Skill. Each one adds UberLLM's hosted MCP server (https://api.uberllm.dev/mcp, Streamable HTTP) and/or the uberllm skill, which knows how to set UberLLM up, pick a model and make images and videos. Nothing to build or run locally: the server is hosted, and it authenticates with your key as Authorization: Bearer ull_....
MCP tools: list_models, get_model, quote, generate_image, edit_image, start_video, get_job, wait_job, balance. A tool call is billed exactly like the same API request. Full reference: https://uberllm.dev/docs/mcp
Claude Code plugin (MCP server + skill). Inside Claude Code:
/plugin marketplace add PSA-Team-source/uberllm
/plugin install uberllm@uberllm
Claude Code asks for your API key when the plugin is enabled and keeps it in your system's credential store. Set or change it later with claude plugin configure uberllm.
Codex plugin (MCP server + skill):
codex plugin marketplace add PSA-Team-source/uberllm
codex plugin add uberllm@uberllm export UBERLLM_API_KEY=ull_... # the MCP server reads the key from here
Gemini CLI extension (MCP server + skill):
gemini extensions install https://github.com/PSA-Team-source/uberllm
Gemini CLI asks for your API key on install and keeps it in your system keychain (change it with gemini extensions config uberllm). If you skip it, export UBERLLM_API_KEY=ull_... before starting gemini works too.
Skill only (Claude Code, Codex, Cursor, OpenCode and other agents that read Agent Skills), via skills.sh:
npx skills add PSA-Team-source/uberllm
MCP server only, Claude Code:
claude mcp add --transport http uberllm https://api.uberllm.dev/mcp --header "Authorization: Bearer ull_..."
Cursor (~/.cursor/mcp.json) and other clients that take a URL:
{
"mcpServers": { "uberllm": { "url": "https://api.uberllm.dev/mcp", "headers": { "Authorization": "Bearer ull_..." } } } }
VS Code (.vscode/mcp.json; VS Code asks for the key once and stores it):
{
"inputs": [ { "type": "promptString", "id": "uberllm-key", "description": "UberLLM API key (ull_...)", "password": true } ], "servers": { "uberllm": { "type": "http", "url": "https://api.uberllm.dev/mcp", "headers": { "Authorization": "Bearer ${input:uberllm-key}" } } } }
Claude Desktop (Settings → Developer → Edit Config), through mcp-remote:
{
"mcpServers": { "uberllm": { "command": "npx", "args": ["-y", "mcp-remote", "https://api.uberllm.dev/mcp", "--header", "Authorization:${UBERLLM_AUTH}"], "env": { "UBERLLM_AUTH": "Bearer ull_..." } } } }
The server is listed in the official MCP Registry as dev.uberllm/mcp (server.json).
from openai import OpenAI
client = OpenAI(base_url="https://api.uberllm.dev/v1", api_key="ull_...") r = client.chat.completions.create( model="deepseek/deepseek-v4-flash", messages=[{"role": "user", "content": "Say hello in five words."}], ) print(r.choices[0].message.content)
from anthropic import Anthropic
client = Anthropic(base_url="https://api.uberllm.dev", api_key="ull_...") m = client.messages.create( model="anthropic/claude-haiku-4.5", max_tokens=100, messages=[{"role": "user", "content": "Say hello in five words."}], ) print(m.content[0].text)
Any chat model in the catalog works on both APIs. Model ids are the ids on https://uberllm.dev/models.
import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
import { generateText } from "ai";
const uberllm = createOpenAICompatible({ name: "uberllm", apiKey: process.env.UBERLLM_API_KEY, baseURL: "https://api.uberllm.dev/v1", includeUsage: true, });
const { text } = await generateText({ model: uberllm("deepseek/deepseek-v4-flash"), prompt: "Say hello in five words.", });
Streaming, tool calls and images: https://uberllm.dev/docs/ai-sdk
mkdir -p ~/.uberllm && chmod 700 ~/.uberllm
printf '%s\n' 'ull_your_key' > ~/.uberllm/key && chmod 600 ~/.uberllm/key
~/.claude/settings.json (every project) or /.claude/settings.local.json (one project): {
"apiKeyHelper": "cat ~/.uberllm/key", "env": { "ANTHROPIC_BASE_URL": "https://api.uberllm.dev", "CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY": "1" } }
install/claude-code.sh (macOS, Linux) and install/claude-code.ps1 (Windows) are byte-for-byte the files served at https://uberllm.dev/install/. They sign you in through the browser, save the key to a mode-600 file, merge ANTHROPIC_BASE_URL and an apiKeyHelper into your Claude Code settings (backing the file up first), and make one test request. --uninstall reverts it.
Read it, check it, then run it:
curl -fsSL https://uberllm.dev/install/claude-code.sh -o claude-code.sh
shasum -a 256 claude-code.sh # compare with install/SHA256SUMS less claude-code.sh sh claude-code.sh # add --project for this project only
| File | SHA-256 |
|---|---|
claude-code.sh | f5aa1c4610992d8f4ecc1b1cd777bc731c5fad9da6a38cac46989834b194d66c |
claude-code.ps1 | 60d631b48e60a2e3f17e5eee19136d47a255f143627986e86b6ceb379858a3b9 |
If the file you downloaded does not match the sum above, do not run it, and tell us at support@uberllm.dev.
Details: https://uberllm.dev/docs/claude-code
support@uberllm.dev · Privacy · Terms
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