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AetherWave Studio

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Edit image with AI (I2I)

aetherwave_edit_image

Edits an existing image guided by a text prompt. Pass a public imageUrl plus a prompt describing the change ("add a moon to the sky", "swap the background for a neon city", "make it look like a comic panel"). Submits, polls, and returns the edited image URL(s). Default model is 'grok-imagine-i2i' (10 cr, one image, ~15-30s, best cost-to-quality on standard edits). Other I2I-capable models: 'seedream-v4-edit', 'wan-2.5-spicy-i2i', 'flux-kontext-pro', 'qwen-image-edit', 'gpt-image-1.5-i2i' (slow, ~5min). Use list_image_models for full lineup. Note: source URLs with spaces or parentheses may fail upstream; prefer clean URLs.

Model selection guide for edits

Default: grok-imagine-i2i (10 cr, one image, fast ~15-30s, strong general-purpose edit quality).

Pick a different model when:

  • Need a single deterministic output, or 4K resolution -> seedream-v4-edit (7 cr per image, supports 1K/2K/4K, multi-image up to 6)

  • Subtle edits / preserve composition / character consistency -> flux-kontext-pro or flux-kontext-max

  • NSFW edits -> wan-2.5-spicy-i2i

  • Highest quality, time is not a concern (~5 min OK) -> gpt-image-1.5-i2i (6 cr medium, 31 cr high) or grok-imagine-quality-i2i (14 cr)

If the user simply says "edit this image" with no other signal, default to grok-imagine-i2i.

Returns within ~45 s. If the result has state "running" the job is still rendering: poll aetherwave_get_job with the returned taskId and kind every ~10 s. Pass wait:true ONLY if your MCP client permits tool calls longer than 60 s (most do not). Pass async:true to get the taskId back immediately without waiting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNoBlock until the job finishes (up to the tool's full budget, 6-15 minutes). ONLY if your MCP client permits tool calls longer than 60 s; most abandon the call at 60 s and lose the taskId. Default false: return within ~45 s and hand back a taskId if still running.
asyncNoSubmit and return the taskId immediately without polling at all. Then poll aetherwave_get_job(taskId, kind).
modelNoModel ID. Defaults to 'grok-imagine-i2i' (10 cr, one image). Other options: 'seedream-v4-edit', 'wan-2.5-spicy-i2i', 'flux-kontext-pro', 'qwen-image-edit', 'gpt-image-1.5-i2i', 'grok-imagine-quality-i2i'. Use list_image_models for the full list.
promptYesText description of the edit (e.g. 'replace the sky with sunset clouds').
qualityNoQuality preset for models that support it (e.g. GPT Image 2).
imageUrlYesPublic URL of the source image to edit. Must be a real, fetchable URL.
maxImagesNoNumber of variations to return for multi-output models.
resolutionNoOutput resolution. Tiered-pricing models accept '1K' / '2K'.
aspectRatioNoOutput aspect ratio (e.g. '1:1', '16:9'). Defaults to the source ratio for most models.
renderingSpeedNoRendering speed preset for models that support it.
negative_promptNoWhat to avoid in the output (supported by some models).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / model / description
      Previous value: -"Model ID. Defaults to 'grok-imagine-i2i' (6 cr, one image). Other options: 'seedream-v4-edit', 'wan-2.5-spicy-i2i', 'flux-kontext-pro', 'qwen-image-edit', 'gpt-image-1.5-i2i', 'grok-imagine-quality-i2i'. Use list_image_models for the full list."New value: +"Model ID. Defaults to 'grok-imagine-i2i' (10 cr, one image). Other options: 'seedream-v4-edit', 'wan-2.5-spicy-i2i', 'flux-kontext-pro', 'qwen-image-edit', 'gpt-image-1.5-i2i', 'grok-imagine-quality-i2i'. Use list_image_models for the full list."
  2. Changed3 schema fields changed
    • addedInput schema / properties / async
      Added value: +{
+  "description": "Submit and return the taskId immediately without polling at all. Then poll aetherwave_get_job(taskId, kind).",
+  "type": "boolean"
+}
    • changedInput schema / properties / model / description
      Previous value: -"Model ID. Defaults to 'grok-imagine-i2i' (3 cr/image effective, 2 outputs). Other options: 'seedream-v4-edit', 'wan-2.5-spicy-i2i', 'flux-kontext-pro', 'qwen-image-edit', 'gpt-image-1.5-i2i', 'grok-imagine-quality-i2i'. Use list_image_models for the full list."New value: +"Model ID. Defaults to 'grok-imagine-i2i' (6 cr, one image). Other options: 'seedream-v4-edit', 'wan-2.5-spicy-i2i', 'flux-kontext-pro', 'qwen-image-edit', 'gpt-image-1.5-i2i', 'grok-imagine-quality-i2i'. Use list_image_models for the full list."
    • addedInput schema / properties / wait
      Added value: +{
+  "description": "Block until the job finishes (up to the tool's full budget, 6-15 minutes). ONLY if your MCP client permits tool calls longer than 60 s; most abandon the call at 60 s and lose the taskId. Default false: return within ~45 s and hand back a taskId if still running.",
+  "type": "boolean"
+}
  3. First observed

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare openWorldHint=true, readOnlyHint=false, idempotentHint=false, but the description adds the operational facts an agent actually needs: per-model credit cost, ~15-30s and ~45s latency, the 6-15 minute blocking budget, the recommendation to poll aetherwave_get_job every ~10s on state 'running', and the caveat that source URLs with spaces or parentheses may fail upstream. That is well beyond what the annotations convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well organized with a front-loaded summary followed by a headed 'Model selection guide for edits' section, and the polling/wait advice comes last. It loses a point for duplication: the model lineup and the wait/async semantics are already spelled out in the schema parameter descriptions, so the description repeats them.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex 11-parameter, multi-model image-edit tool with no output schema, the description covers submission, latency expectations, polling handoff to aetherwave_get_job, wait vs async tradeoffs, model/cost selection, and upstream URL constraints. An agent has everything needed to invoke it correctly and recover from a long-running job.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3, but the description adds genuine meaning over the schema: it maps model IDs to cost/quality/latency tradeoffs, reiterates the clean-URL constraint on imageUrl, and explains the practical failure of wait:true in typical MCP clients. The remaining parameters (quality, resolution, aspectRatio, maxImages, negative_prompt, renderingSpeed) are left entirely to the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Specific verb+resource ('Edits an existing image') with the mechanism named ('guided by a text prompt') and concrete examples of prompts. It is clearly separable from siblings like aetherwave_generate_image (creation), aetherwave_reframe_image, and aetherwave_upscale_image because it operates on a supplied source image.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states the default model and then gives a condition->model routing table (deterministic/4K -> seedream-v4-edit, subtle edits -> flux-kontext, NSFW -> wan-2.5-spicy-i2i, highest quality -> gpt-image-1.5-i2i). It even handles the ambiguous case ('if the user simply says edit this image, default to grok-imagine-i2i') and names list_image_models as the discovery fallback.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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