Image Tool

Image Description

Analyze images using AI vision models.

Endpoint

POST /v1/image/describe

Request

curl https://api.router9.com/v1/image/describe \
  -H "Authorization: Bearer sk-r9k-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "mediaUrl": "https://example.com/photo.jpg",
    "prompt": "What is in this image?"
  }'

Response

{
  "model": "google/gemini-3.5-flash",
  "text": "The image shows a modern office space with developers working at their desks, multiple monitors displaying code editors."
}

Image Generation

Generate images from a text prompt.

Endpoint

POST /v1/image/generations

Request

curl https://api.router9.com/v1/image/generations \
  -H "Authorization: Bearer sk-r9k-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "A futuristic city skyline at sunset, digital art",
    "aspect_ratio": "16:9",
    "output_format": "webp"
  }'

/v1/image/generations is text-to-image only. Sending images returns 400 pointing at /v1/image/edits.

Prompt enhancement

enhance_prompt: true runs one extra chat completion that expands a short prompt into a detailed description, then draws from that. Reach for it when you have an instruction rather than a description; skip it when you already said exactly what you want, because the rewrite adds detail you did not ask for.

The text it produced comes back as enhanced_prompt in the response body, and also in the X-Router9-Enhanced-Prompt header — the body is authoritative, since the header is sanitized and capped at 512 characters.

It fails open. If the rewrite cannot be produced for any reason, your own prompt is drawn and nothing extra is billed. There is no separate flag to check: enhanced_prompt is absent exactly when the original was used.

It is ignored on /v1/image/edits. An edit's prompt describes a change, and whatever you do not mention is expected to survive unchanged; an expanded prompt reintroduces the whole scene as instructions.

It is a second billed call, on its own line in your usage — one row for the rewrite and one for the picture.

Parameter support varies by model. A value this endpoint accepts can still be refused upstream; that error is returned verbatim so you can see which model rejected what.

Response

{
  "id": "imggen_01J8Z3C4V9",
  "created": 1714000000,
  "model": "google/gemini-3.1-flash-image-preview",
  "data": [
    {
      "b64_json": "UklGRiQAAABXRUJQ...",
      "media_type": "image/webp",
      "revised_prompt": "A futuristic city skyline at sunset, digital art"
    }
  ],
  "enhanced_prompt": "A futuristic city skyline at sunset, digital art — glass towers catching low amber light, elevated walkways, a haze of traffic below",
  "timing": { "total_ms": 8412, "upstream_ms": 8207 }
}

Images are returned as base64 bytes, never URLs. Use media_type to decode — do not assume PNG, since the default output format is WebP.

enhanced_prompt appears only when you asked for a rewrite and one was produced.

id names this generation in a support thread, and matches the request id in your own logs. timing splits the total against the upstream call, so a slow generation can be attributed without guessing. The vendor's own request id, when they report one, is in the X-Router9-Upstream-Trace-Id response header.

If the model declines to draw, the call still returns 200 with an empty data and a refusal string — and is still billed, because the upstream charged for the attempt either way.

Image Editing

Edit existing images, or guide a generation with references.

Endpoint

POST /v1/image/edits

Request (JSON)

Up to four references, as HTTP(S) URLs or data: URLs:

curl https://api.router9.com/v1/image/edits \
  -H "Authorization: Bearer sk-r9k-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "make this a watercolor painting",
    "images": ["https://example.com/photo.jpg"]
  }'

Request (multipart/form-data)

The shape an OpenAI SDK sends. image may be repeated, or spelled image[]:

curl https://api.router9.com/v1/image/edits \
  -H "Authorization: Bearer sk-r9k-your-key" \
  -F prompt="make this a watercolor painting" \
  -F [email protected]

Every generation parameter above applies here too. The response is the same shape as /generations.

The gateway downloads referenced URLs itself rather than passing them upstream, so private and internal addresses are refused.

Over MCP this is a separate image_edit tool; the response shape is the same.

Upscaling

Enlarge an existing image without redrawing it. No prompt: the subject, composition and detail stay as they are, at a higher resolution.

Endpoint

POST /v1/image/upscale

curl https://api.router9.com/v1/image/upscale \
  -H "Authorization: Bearer sk-r9k-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "image": "https://example.com/photo.jpg",
    "scale": 4
  }'

Background removal

Cut the subject out, leaving the background transparent.

Endpoint

POST /v1/image/background-removal

curl https://api.router9.com/v1/image/background-removal \
  -H "Authorization: Bearer sk-r9k-your-key" \
  -H "Content-Type: application/json" \
  -d '{ "image": "https://example.com/product.jpg" }'

Both answer in the same { id, created, model, data, timing } shape the generation endpoints do, and both count against the Image Generation quota — the allowance is about how many pictures an account produces, not which operation produced them.

Over MCP these are the image_upscale and image_background_remove tools.

Catalog

GET /v1/public/image-models   → { "models": [...], "planLimits": {...} }

Public. Each model carries a constraints block — the aspect ratios, formats, resolutions and features the provider actually accepts, plus the diffusion knobs (negativePrompt, cfgScale, stylePresets, seed) and what the transform endpoints take (upscaleFactors, transformFormats) — so a client can discover a limit without having to be refused by it. A knob the model does not take is reported as {"accepted": false} with no range, because a range of 0–0 reads as an offer. planLimits is separate and stated per tier, because those are the caps your plan adds on top of the vendor's.

Style vocabulary

GET /v1/image/styles   → { "styles": [...], "models": [...] }

The style_preset values each configured model accepts. styles is the default model's set — what you are validated against when you name no model — and models carries the same per model, because two families from one vendor routinely disagree: one takes seventeen presets and the other takes none.

A model that does not accept style_preset at all publishes an empty list rather than the provider's, so nothing here is a value that would be refused.

Errors

Every image endpoint answers failures in the shared media error envelope. See Errors.

Credits

Image Skill calls consume credits from your plan's monthly credit allowance — the same allowance used for chat completions. There are no separate per-Skill quotas. Image models are priced per image or per megapixel, so n and resolution multiply what a single call costs. See Credits & Usage and Tool Usage & Credits.