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

io.github.KuudoAI由 GitHub 账号 KuudoAI 发布v1.0.0更新于 Oct 24, 2026

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

已验证Streamable HTTP可网页运行商业与电商媒体与设计

概览

AI 生成的概览

远程 MCP 服务器，让助手在自有 Kuudo 部署中基于实时商品目录数据生成和编辑亚马逊商品图片与产品视频。

可以做什么
使用 Google Gemini 或 OpenAI 模型生成或编辑商品图片，支持链式精修以及图标、图案、图表、分镜、照片修复等操作预设，并按从技能资源读取的亚马逊图片合规规则校验。创建带有效期的上传链接，以便把本地图片字节送入生成流程。使用 Google Veo 从文本、首尾帧或参考图生成产品视频，并查询、列出和恢复长时间运行的视频任务。读取服务器的资源与提示词，包括亚马逊图片和视频技能文档。查看服务器在认证、模型、存储、上传和健康方面的诊断信息。
适用场景
适合希望让助手按平台规则制作或审核商品图片与产品视频、并运行可恢复的视频渲染任务的亚马逊卖家和商品团队，且需在自有 Kuudo 云部署中使用。
运行要求
需要一个 Kuudo 部署主机，通过 streamable HTTP 连接到该部署的 MCP 地址。需要 Authorization bearer 头，携带 Kuudo API 密钥或部署 bearer 令牌。OpenAI 图片工具另需 OpenAI 密钥，同样以 Authorization bearer 头发送。仓库本身只是仅含目录的桩程序，实际执行只在部署中进行。
安装前请注意
Authorization 头携带 Kuudo API 密钥或部署 bearer 令牌，OpenAI 图片工具还要求在同一头名称中提供单独的提供商密钥。图片和视频生成由底层提供商计费；若视频提交结果未知，不要用新 id 重试，因为 Google 可能已接受并计费。写工具只在你的部署内操作，README 称其不会更改亚马逊上的任何内容。上传链接为签名链接且会过期。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Amazon listing image and video MCP server (Amazon Agent Iris)，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

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.

[MCP] [Registry] [License: MIT] [Community]

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:

json
{  "mcpServers": {    "amazon-agent-iris-mcp": {      "url": "https://<your-host>/mcp",      "headers": {        "Authorization": "Bearer <your Kuudo API key>"      }    }  }}

Replace <your-host> 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.

bash
docker build -t amazon-agent-iris-mcp .docker run -i --rm amazon-agent-iris-mcp

Point a client at it with a stdio entry:

json
{  "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)
ToolAccessWhat it does
start_herereadCall 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_imagewriteGenerate 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_imagewriteGenerate 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 <key> — the tool name implies the provider. Iterate on a result with previous_response_id (OpenAI's analog of Gemini's interaction_id).
create_upload_urlwriteCreate 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_statusreadReturn server diagnostics for auth, models, storage, uploads, resources, and health.
generate_videowriteGenerate 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_statusreadProbe 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_operationsreadList 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_resourcesreadList 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_resourcereadRead 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_promptsreadList all available prompts. Returns JSON with prompt metadata including name, description, and optional arguments.
get_promptreadGet 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

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

Generated from KuudoAI/marketing. Do not edit by hand; changes are overwritten on the next sync.

来源：README.md，提交 3dafb84

工具

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版本历史

1
  1. v1.0.0最新Sep 16, 2026

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