MCP server that creates Amazon listing images and product video from live catalog data.
Quick Install
Automated & IDE SetupCopy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
Manual Client & Custom JSON ConfigExpand JSON ▾
Client Config & Setup
Choose your client or environment
Target File:
💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Documentation Overview
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:
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.
Point a client at it with a stdio entry:
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 <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_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. |
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