three.ws Vision

io.github.nirholas由 GitHub 帳號 nirholas 發布v0.1.2更新於 Oct 24, 2026

Image understanding for AI agents — analyze and describe any image via the three.ws pipeline.

已驗證STDIO僅桌面媒體與設計AI 與機器學習

概覽

AI 產生的概覽

本機 stdio MCP 伺服器，透過 three.ws 讓助理具備影像理解能力：針對圖片提問、讀取截圖文字、產生替代文字。

可以做什麼
  • 針對圖片回答提示詞：視覺問答、截圖 OCR、分類或渲染圖評論
  • 產生自然語言描述與替代文字，可依詳細程度與關注重點調整
  • 查詢視覺服務是否上線以及支援哪些圖片格式
  • 接受公開 https 圖片網址或 base64 / data URI 輸入
適用情境
當助理需要查看自己無法直接看到的圖片時適用，例如讀取截圖中的文字、產生說明或替代文字、辨識畫面內容，或檢視 3D 與虛擬人像渲染圖。
執行需求
需要 Node.js 20 或更新版本，可透過 npx 或 npm 安裝執行。需要能連線到 three.ws 端點（或自訂 THREE_WS_BASE）。可選用 THREE_WS_API_KEY 作為 bearer 權杖，以及 THREE_WS_TIMEOUT_MS；免費層不需金鑰。圖片須為 JPEG、PNG、WebP 或 GIF，最大 12 MB，imageUrl 必須是公開 https 網址。
安裝前請注意
圖片（包含 base64 內容）會傳送到 three.ws 服務進行分析，請勿提交機密截圖。請求依 IP 限流；可選的 THREE_WS_API_KEY 可提高上限，並以 bearer 權杖形式傳送。工具為唯讀，但結果不具確定性，後端模型鏈也可能變動。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 three.ws Vision，將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only，透過 STDIO。 STDIO 服務會啟動本機處理程序，因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

[three.ws]

@three-ws/vision-mcp

Let any AI agent see — analyze and describe images through the three.ws vision pipeline, free-first and no key required.

[npm] [license] [node] [MCP Registry] [three.ws]

A Model Context Protocol server that gives any AI assistant image understanding over stdio. Pass an image as a public URL or base64 and the agent can read the text in a screenshot, identify what's in a scene, critique a 3D/avatar render, or write alt-text — all live, read-only, no key required.

Every answer comes from the three.ws vision pipeline: free NVIDIA NIM vision models lead every request, with an automatic server-side paid backstop behind them. The caller never pays and needs no provider key — point THREE_WS_BASE at a deployment and go.

Install

bash
npm install @three-ws/vision-mcp

Or run with npx (no install):

bash
npx @three-ws/vision-mcp

Quick start

Claude Code, one line:

bash
claude mcp add vision -- npx -y @three-ws/vision-mcp

Claude Desktop / Cursor (claude_desktop_config.json or mcp.json):

json
{	"mcpServers": {		"vision": {			"command": "npx",			"args": ["-y", "@three-ws/vision-mcp"]		}	}}

Inspect the surface with the MCP Inspector:

bash
npx -y @modelcontextprotocol/inspector npx @three-ws/vision-mcp

Tools

ToolTypeWhat it does
analyze_imageread-onlyAnswer a prompt about an image — visual Q&A, OCR (read a screenshot), classification, or critiquing a render.
describe_imageread-onlyNatural-language description for alt text or a caption, steerable by detail level and focus.
get_vision_statusread-onlyReport whether a vision provider is live and which image formats are accepted. No image, no cost.

All three tools are read-only — they look at an image, they never store or mutate anything. A VLM is non-deterministic and the live provider chain moves, so none are idempotent.

Input parameters

analyze_image — prompt (required), and exactly one of imageUrl (public https) or image (base64 / data URI). Optional imageType (for raw base64), maxTokens (16–2048, default 512).

describe_image — exactly one of imageUrl or image. Optional imageType, detail (brief | standard | detailed, default standard), focus (an aspect to emphasize).

get_vision_status — no parameters.

Example

jsonc
// analyze_image: read the text in a logo image> { "imageUrl": "https://three.ws/logo.png", "prompt": "What does this logo say?" }{  "ok": true,  "text": "The logo reads \"three.ws\".",  "provider": "nvidia",  "model": "nvidia/nemotron-nano-12b-v2-vl",  "prompt": "What does this logo say?",  "image_source": "url"}
jsonc
// describe_image — alt text from a base64 image> { "image": "data:image/png;base64,iVBORw0KG...", "detail": "brief" }{  "ok": true,  "description": "A seagull in profile against a blurred coastal background.",  "detail": "brief",  "provider": "nvidia",  "model": "nvidia/nemotron-nano-12b-v2-vl",  "image_source": "base64"}
jsonc
// get_vision_status — gate a "describe this" affordance> {}{  "ok": true,  "configured": true,  "image_types": ["image/jpeg", "image/png", "image/webp", "image/gif"],  "max_image_mb": 12}

Examples

Runnable, key-free examples live in examples/:

bash
node examples/list-tools.mjs     # every tool with its full input schemanode examples/read-an-image.mjs  # status + alt text + OCR on a live image

Both spawn this server over stdio and call the real vision pipeline. Every tool is read-only, so nothing is stored and nothing is paid. See examples/README.md for expected output.

Images may be JPEG, PNG, WebP, or GIF, up to 12 MB. Base64 inputs are size-checked locally before upload; imageUrl must be a public https URL (the vision server fetches it, so private/loopback hosts are rejected).

Pricing & auth

There is no charge to the caller and no key is needed. The platform runs every request against free NVIDIA NIM vision lanes first and falls back to a paid model only on its own infrastructure — that cost is the platform's, transparent to you.

The endpoint is rate-limited per IP. To raise your limit to the per-user tier, set the optional THREE_WS_API_KEY (a three.ws API key); it is sent as a bearer token. It is never required to use the free model.

Requirements

  • Node.js >= 20.
  • Network access to https://three.ws (or your own THREE_WS_BASE).

Environment variables

VariableRequiredDefaultPurpose
THREE_WS_BASEnohttps://three.wsWhich deployment to call.
THREE_WS_TIMEOUT_MSno30000Per-request timeout (a call may chain across free lanes).
THREE_WS_API_KEYno—Optional bearer token to raise the rate limit to per-user.

Links

Part of the three.ws SDK suite — 3D AI agents, on-chain identity, and agent payments.
Website · Changelog · GitHub

來源：packages/vision-mcp/README.md，提交 c8f2cde

工具

0
SourceWeft 尚未在沙箱中啟動過該軟體包，因此其工具未知。

版本歷史

2
  1. v0.1.2最新Oct 10, 2026
  2. v0.1.1Sep 16, 2026

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