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

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

by KuudoAIversion 1.0.0i★ –iRuns in the cloud

Registry nameio.github.KuudoAI/amazon-agent-iris-mcp

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs in the cloud
Transport: Streamable HTTP
Category: Design, images & audio

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on the maker’s server: what you ask it goes through their server.
You fill in yourself: Authorization

You need

Runs in the cloudNothing to install; it connects over the internet

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.

Run this in your terminal

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

Fill in the empty values before running it.

The address contains {…} parts that only you know (such as your account), so copy it and fill them in.

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

The address contains {…} parts that only you know (such as your account), so copy it and fill them in.

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This address needs a key in a request header, which the add-connector menu cannot set. Use Claude Code, Cursor or VS Code instead.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

Authorization HTTP header
Kuudo API key or deployment bearer token.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

We have not read this server’s tool list yet; we check endpoints in rotation.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account KuudoAI, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
The address needs your own account values, so we cannot check it for you
– Not applicable
Sign-in with OAuth
The address needs your own account values, so we cannot check it for you
… Not checked yet
No hidden instructions in tool descriptions
Not checked yet; we read the tool list when we check the endpoint

Compared with others in Design, images & audioi

More stars than 0% of the other 2764 servers in this category
Published to the registry more recently than 29% of the other 2764 servers in this category
Basic checks equal to or better than 100% of the other 2764 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Amazon listing image and video MCP server (Amazon Agent Iris) safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs in the maker’s cloud. Connect over the internet with nothing to install, but what you send passes through the maker’s servers.

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.0) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. It connects by URL, so apps that accept remote MCP servers work too, such as ChatGPT in developer mode.

What can I use instead?

In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Figma Context MCP

by GLips

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

Runs on your computer★ 16,165MITUpdated 5 months ago

Install

Claude Real Video

by HUANGCHIHHUNGLeo

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

Runs on your computer★ 2,284MITUpdated 2 months ago

Install

Figma MCP Server

by figma

Figma’s official server gives AI your design data so it can turn designs into code or describe them accurately. Connects to Figma’s cloud.

Runs in the cloud★ 2,152Updated 13 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

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

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Design, images & audioi

Common questions

Alternatives

Figma Context MCP

Claude Real Video

Figma MCP Server

README from the maker

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

Connect

What this repository is

Inspect the catalog locally with Docker

Tools

Set up your client

Playbooks

How it compares

Reads, writes, approvals

Kuudo