generate_image

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about model capabilities (resolution, speed, thinking mode) and tips for effective prompting, but it does not disclose critical behavioral traits such as rate limits, authentication requirements, cost implications, or what happens on failure (e.g., error handling). The description is informative but incomplete for safe operation.

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 well-structured and appropriately sized, with a clear purpose statement followed by model details and tips. Every sentence earns its place by providing actionable information, though the tips section could be slightly more concise. It is front-loaded with the core functionality.

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 tool's complexity (image generation with multiple parameters) and lack of annotations or output schema, the description is moderately complete. It covers purpose, model options, and usage tips, but it lacks details on output format (e.g., file type, return structure), error conditions, and operational constraints like rate limits or costs, which are important for a generative AI tool.

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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds marginal value by reinforcing prompt specificity and mentioning aspect ratio in the tips, but it does not provide additional semantic meaning beyond what the schema descriptions already cover (e.g., the schema's prompt description says 'Be descriptive and specific,' mirroring the tips). Baseline 3 is appropriate when the schema does the heavy lifting.

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 the tool's purpose: 'Generate an image from a text prompt using Google Gemini's image generation.' This specifies the verb ('generate'), resource ('image'), and technology ('Google Gemini's image generation'), distinguishing it from sibling tools like 'compose_images' and 'edit_image' which imply different operations.

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 provides clear context for when to use this tool (image generation from text prompts) and includes tips for better results, but it does not explicitly state when to use alternatives like 'compose_images' or 'edit_image'. The model descriptions ('Best for high-volume tasks' vs 'Best for professional assets') offer some guidance, but no explicit exclusions or comparisons to siblings are provided.

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