generate_image

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

With no annotations, the description carries the full burden and does well: it explains the tool generates new images (not edits), details reference_image format restrictions (file path/base64, no Claude image format), and describes creative_mode/layout_freedom behavioral differences. However, it does not disclose potential issues like API costs, rate limits, or error behavior.

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 front-loaded with a clear purpose and sibling differentiation, then follows with a structured Args list. Every sentence adds value, and while long, the length is justified by 17 parameters needing explanation. There is no repetition of schema fields beyond what is necessary.

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?

For a tool with 17 parameters and no annotations, the description is remarkably complete. It covers the tool's purpose, content types, when to use an alternative, and enriches every parameter with semantic detail. The presence of an output schema means return-value documentation is not required, so no major gaps remain.

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 0%, and the description compensates by documenting all 17 parameters in the Args list with clear meanings and allowed values. For example, size is 'auto-selected based on content_type,' quality lists low/medium/high/auto, style provides visual style options, and reference_image includes format constraints. This fully bridges the gap left by the schema.

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 description clearly states it generates images using OpenAI gpt-image-1 for YouTube thumbnails, blog images, and social media content, and explicitly says it creates 'completely new images from text descriptions.' It also distinguishes itself from the sibling tool generate_reference_thumbnail by noting that tool is for incorporating reference images, giving it a clear identity.

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 description gives explicit guidance for when to use this tool vs. generate_reference_thumbnail: 'For incorporating reference images (especially for YouTube thumbnails with people), use generate_reference_thumbnail instead.' It also implies general use cases. However, it does not mention other sibling tools like optimize_for_platform or generate_batch, so not fully covers all alternatives.

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