ai_image_generator_create_image

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It clearly states that the call starts an async job, returns id and credits_charged immediately, enumerates final statuses, and explains how completed projects expose downloads. This is thorough and directly actionable.

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

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

The description is compact and front-loaded: one sentence states the purpose, then a bullet of MCP guidance adds the essential async workflow. Every sentence earns its place, and there is no fluff or repetition of schema content.

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

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

Given the rich output schema and comprehensive parameter documentation, the description fills the critical gap the schema cannot: async behavior and how to obtain finished results. It names the helper, enumerates statuses, and notes download URLs, so an agent has everything needed to invoke and follow up correctly.

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

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

Schema coverage is 100% and every parameter is documented in detail, including model constraints, resolution options, and style defaults. The description adds little parameter-level meaning beyond the phrase 'advanced model selection and quality controls,' so the baseline 3 is appropriate.

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

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

The opening sentence identifies a specific action ('Create an AI image') and resource ('AI image') with a nod to model selection and quality controls. It does not explicitly differentiate this general-purpose generator from specialized siblings like ai_face_editor_create_image or ai_clothes_changer_create_image, though the name and 'advanced model selection' hint at its general role.

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

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

The MCP guidance gives clear post-call instructions (call wait_for_image_project or poll GET /v1/image-projects/{id}), but it says nothing about when to select this tool vs the many sibling image generators. There are no alternative-based exclusions, so selection context is mostly left to the tool name and implied generality.

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