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AIWerk

@aiwerk/mcp-server-elevenlabs

by AIWerk

create_image_generation

Generate or edit images from text prompts and reference images using ElevenLabs models, with control over aspect ratio, resolution, quality, and background.

Instructions

Create Image Generation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maskNo
seedNo
imagesNoUp to 10 reference images to edit or draw from.
promptNoA text description of the image to generate.
qualityNoThe quality of the output image.
webhookNo
model_idNoThe model to use for the generation.
backgroundNoThe background of the output image. With `auto`, the model picks the background that suits the image.
resolutionNoThe resolution of the output image.
aspect_ratioNoThe aspect ratio of the output image. With `auto`, the model picks an aspect ratio based on the inputs.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

D1.7/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false, so the safety profile is covered structurally. The description adds nothing beyond that — no mention of async behavior, cost implications, model selection consequences, or rate limits.

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

Conciseness2/5

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

Extremely short but not in a good way; this is under-specification rather than conciseness. There is nothing front-loaded because there is nothing substantive to load.

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

Completeness1/5

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

For a 10-parameter generative tool with no output schema and an async webhook parameter, the definition is completely inadequate. An agent cannot know required inputs, output format, or completion semantics from this description.

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

Parameters2/5

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

Schema description coverage is 70%, below the 80% baseline, so the description should compensate for the undocumented parameters (seed, model_id, images items) — but it provides zero parameter meaning. An agent must rely entirely on the schema.

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

Purpose2/5

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

The description 'Create Image Generation' merely restates the tool name and title verbatim, providing no additional specificity beyond the tautology. It does not clarify scope, distinguish from siblings like create_video_generation or text_to_voice_design, or state what kind of image generation is performed.

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

Usage Guidelines1/5

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

There is no guidance whatsoever about when to use this tool versus alternatives. With many sibling creation tools (create_video_generation, sound_generation, generate), the agent receives no routing signal.

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

Deploy Server

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