Amazon listing image and video MCP server (Amazon Agent Iris)

Name: Amazon listing image and video MCP server (Amazon Agent Iris)
Author: KuudoAI

作者：KuudoAI版本 1.0.0i★ –i在云端运行

注册名称io.github.KuudoAI/amazon-agent-iris-mcp

安装

概览

作者提供的说明（英文原文）

MCP server that creates Amazon listing images and product video from live catalog data.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

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。

连接方式: 在云端运行
传输方式: Streamable HTTP
分类: 设计、图片与音频

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在作者的服务器上运行：你向它提出的内容会经过他们的服务器。
需要你自己填写： Authorization

你需要

在云端运行无需安装任何东西，通过互联网连接

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

这个地址需要在请求的 header 中放入密钥，而添加连接器的菜单无法设置。请改用 Claude Code、Cursor 或 VS Code。

在终端中运行这条命令

claude mcp add --transport http amazon-agent-iris 'https://{AGENT_IRIS_HOST}/mcp' --header 'Authorization: Bearer {AUTH_TOKEN}'

运行前请先填写空着的值。

地址中有只有你知道的 {…} 部分（例如你的账户），请复制后自行填写。

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "amazon-agent-iris": {
      "url": "https://{AGENT_IRIS_HOST}/mcp",
      "headers": {
        "Authorization": "Bearer {AUTH_TOKEN}"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

地址中有只有你知道的 {…} 部分（例如你的账户），请复制后自行填写。

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "amazon-agent-iris": {
      "type": "http",
      "url": "https://{AGENT_IRIS_HOST}/mcp",
      "headers": {
        "Authorization": "Bearer {AUTH_TOKEN}"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个地址需要在请求的 header 中放入密钥，而添加连接器的菜单无法设置。请改用 Claude Code、Cursor 或 VS Code。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

Authorization HTTP header
Kuudo API key or deployment bearer token.

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。

它能做什么

我们还没有读取这个服务器的工具列表；我们会轮流检查各个端点。

基础检查i

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8日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 KuudoAI 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
这个地址需要填入你自己的账户信息，所以我们无法替你检查
– 不适用
使用 OAuth 登录
这个地址需要填入你自己的账户信息，所以我们无法替你检查
… 尚未检查
工具描述中没有隐藏指令
尚未检查；我们会在检查端点时读取工具列表

与“设计、图片与音频”中的其他服务器比较i

星标数多于此分类中其他 2767 个服务器的 0%
发布到注册中心的时间比此分类中其他 2767 个服务器的 29% 更新
基础检查结果等于或优于此分类中其他 2767 个服务器的 100%

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。

常见问题

Amazon listing image and video MCP server (Amazon Agent Iris) 安全吗？

它在 1 项适用的基础检查中通过了 1 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。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

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。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.0.0）于2个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接，所以接受远程 MCP 服务器的应用也可以使用，例如开启开发者模式的 ChatGPT。

有什么可以替代它？

同一分类中有：Figma Context MCP, Claude Real Video, Figma MCP Server

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ 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

Figma Context MCP

作者：GLips

Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.

在你的电脑上运行★ 16,165MIT5个月前更新

安装

Claude Real Video

作者：HUANGCHIHHUNGLeo

Let any LLM watch a video locally — and search everything it has ever watched.

在你的电脑上运行★ 2,284MIT2个月前更新

安装

Figma MCP Server

作者：figma

The Figma MCP server brings Figma design context directly into your AI workflow.

在云端运行★ 2,15213个月前更新

安装

作者的 README

打开 README

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。

Amazon listing image and video MCP server (Amazon Agent Iris) by Kuudo

Create Amazon listing images and product video from live catalog data, real product references, Amazon rules, and secure, human-approved workflows.

Product page: https://www.kuudo.com/features/amazon-agent-iris/ · Docs: https://www.kuudo.com/docs/amazon-agent-iris/ · Pricing: https://www.kuudo.com/pricing.md

Connect

Kuudo runs in your own cloud. The Community plan deploys one instance of each Amazon MCP server into your account, and your client connects to that deployment:

"mcpServers": { "amazon-agent-iris-mcp": { "url": "https:// /mcp", "headers": { "Authorization": "Bearer " } } } }

Replace with the hostname of your deployment and the bearer value with your Kuudo API key.

A tool may document a provider credential of its own. That is separate from the bearer above, it belongs to the service the tool calls, and the tool's own entry says so.

What this repository is

This repository holds registry metadata and a catalog-only stub. Live execution runs in your Kuudo deployment. The server source is not published. The stub in src/ answers tools/list with the catalog below, serves the same catalog as one resource (kuudo://catalog/tools.json), offers one prompt (connect) carrying the setup guidance, and returns an error with that guidance on any call, so registries and clients can inspect the surface without any access to Amazon.

Inspect the catalog locally with Docker

The image runs the same catalog-only stub over stdio. It is not the live server.

docker build -t amazon-agent-iris-mcp .

docker run -i --rm amazon-agent-iris-mcp

Point a client at it with a stdio entry:

"mcpServers": { "amazon-agent-iris-mcp-catalog": { "command": "docker", "args": ["run", "-i", "--rm", "amazon-agent-iris-mcp"] } } }

Tools

12 tools, read from a running instance of this server. Four of the twelve mirror the MCP resource and prompt methods, and two more are onboarding and server health. The remaining six generate images and video, hand back an upload URL for local source bytes, and track video jobs.

Each tool carries the argument schema the live server publishes, so a client can inspect the full call signature here before it connects to your deployment.

Every tool (12)

Tool	Access	What it does
`start_here`	read	Call this FIRST to understand how to use this image and video generation server. Returns workflow documentation for: - Available tools - HTTP image uploads - Editing photos - Long-running, resumable video generation - Generating new images - Downloading results
`generate_image`	write	Generate or edit images with Google Gemini (Nano Banana) from natural language instructions. For OpenAI gpt-image-2, use the sibling `generate_openai_image` tool. For Amazon / Seller Central listing images, FIRST read the `skill://amazon-product-image/SKILL.md` resource (via `read_resource`) for the marketplace image-compliance rules, then generate/audit against them. ## CHARACTER CONSISTENCY Use public image handles for character-consistent image generation: `generate_image( input_images=["agent-iris://images/img_12"], mode="generate", prompt="PRESERVE EXACTLY: character's facial features. NEW: Professional headshot...", model="gemini-3-pro-image" )` Explicit generate mode uses the image handle as conditioning rather than an edit source. ## EDITING AN EXISTING IMAGE For local bytes, upload with POST /images first, then pass the returned handle. For a public hosted image, pass the HTTPS URL directly: `generate_image( input_images=["https://example.com/source.jpg"], mode="edit", prompt="Change the background to a beach sunset" )` ## ITERATING ON A RESULT (high-fidelity chained edit) — PREFERRED Every generate/edit response returns an `interaction_id`. Pass it back to refine the SAME image while preserving the rest of the composition — no need to re-send the source. This beats re-editing from bytes for edit fidelity: `generate_image( interaction_id="v1_Chd...", # from the previous response prompt="Recolor the mug to beige. Change nothing else." )` TTL: ~55 days (paid) / 1 day (free). ## COMPOSITING (Person from image A into scene B) Use multiple references with generate mode: `generate_image( input_images=["agent-iris://images/img_12", "gemini://files/beachRef"], mode="generate", prompt="Place character from image 1 into the beach scene from image 2..." )` ## OPERATION PRESETS Use operation="icon", "pattern", "diagram", "storyboard", or "photo_repair" for strict defaults around model, aspect ratio, and prompt framing. ## Mode Auto-Detection: - interaction_id → EDIT mode - operation="photo_repair" → EDIT mode - operation="icon"/"pattern"/"diagram"/"storyboard" → GENERATE mode - operation="general" with one input image → EDIT mode - Multiple input images or pure prompt → GENERATE mode Returns both MCP image content blocks and structured JSON with metadata.
`generate_openai_image`	write	Generate images with OpenAI gpt-image-2 via the Responses API. The additive OpenAI provider, sibling to `generate_image` (Google Gemini). Send your OpenAI key as `Authorization: Bearer` — the tool name implies the provider. Iterate on a result with `previous_response_id` (OpenAI's analog of Gemini's `interaction_id`).
`create_upload_url`	write	Create the expiring signed URL needed to upload LOCAL image bytes. Use this when you have an image on disk (or raw bytes) to feed into generate_image: call this tool, then HTTP `POST` the bytes to the returned `upload_url` (no auth header needed — the URL's signed token authorizes it). The upload responds with a `agent-iris://images/{id}` handle to pass in generate_image's `input_images`. Hosted http(s) image URLs do NOT need this — pass them to generate_image directly.
`server_status`	read	Return server diagnostics for auth, models, storage, uploads, resources, and health.
`generate_video`	write	Generate video with Google Veo 3.1 from text, a first image, first/last frames, up to three reference images, or a Veo-generated video to extend. Inputs are bounded handles or http(s) URLs, never inline data; references, extensions, and 1080p/4k output require an 8-second request. Before planning or generating video, read the canonical `skill://amazon-video/SKILL.md` resource. It covers mode and model selection, prompt craft, recovery, and the additional compliance workflow for Amazon listing videos. Renders take 11 s to several minutes. Task-capable clients run this in the background; other clients receive `pending` and resume with `get_video_status(recovery_id)`. Reuse the same `recovery_id` after a timeout. If submission is reported as unknown, do not retry under a new id because Google may already have accepted the billed request. Results are signed download URLs and `agent-iris://videos/vid_N` handles.
`get_video_status`	read	Probe one video operation (exactly one of `recovery_id` / `operation_name`). Makes at most one Google status call and never waits for rendering. When the render is finished it downloads and stores the video in this call. Owner-scoped: only the key that submitted the operation can see it.
`list_video_operations`	read	List the video operations submitted with this key, newest first. Reads only the local operation store — no Google call. Use a listed `recovery_id` with `get_video_status` to resume or collect.
`list_resources`	read	List all available resources and resource templates. Returns JSON with resource metadata. Static resources have a 'uri' field, while templates have a 'uri_template' field with placeholders like {name}.
`read_resource`	read	Read a resource by its URI. For static resources, provide the exact URI. For templated resources, provide the URI with template parameters filled in. Returns the resource content as a string. Binary content is base64-encoded.
`list_prompts`	read	List all available prompts. Returns JSON with prompt metadata including name, description, and optional arguments.
`get_prompt`	read	Get a prompt by name with optional arguments. Returns the rendered prompt as JSON with a messages array. Arguments should be provided as a dict mapping argument names to values.

Set up your client

Playbooks

Operator guides grounded in Amazon's own documentation, each with the artifact the agent produces:

How it compares

Kuudo vs Pixii for Amazon sellers

Reads, writes, approvals

Read tools are safe to call freely. Write tools do work inside your deployment and never change anything on Amazon. Each tool's access is recorded beside it in tools.json.

Kuudo

Website: https://www.kuudo.com/
Docs: https://www.kuudo.com/docs/
Guides: https://www.kuudo.com/guides/
Community, bugs, and questions: https://github.com/KuudoAI/community
Roadmap: https://github.com/orgs/KuudoAI/projects
Machine-rea

只显示了开头部分；其余内容请在仓库中阅读