labeled_image

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

With no annotations, the description carries the full burden of behavioral disclosure. It usefully discloses that the tool selects randomly among the top_k best cases, that seed controls reproducibility, and that shuffle_labels randomizes label-to-group assignment. It does not describe side effects such as writing to output_dir, what is returned, how failures are handled, or how visibility is evaluated.

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 three tightly written sentences with the core action front-loaded. Each sentence adds distinct information: the find-and-render behavior, the top_k randomness and seed reproducibility, and the shuffle_labels effect. The phrase "answer's number varies" is slightly ambiguous but does not undermine the overall efficiency.

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?

This is a complex tool with 14 parameters, extensive nested objects, no annotations, and no output schema. The description covers only the core selection and randomization semantics. It leaves the agent without enough context about filters, rendering settings, output behavior, include_base64, output_dir, hotspot_zones, strategy, and how labeled structures are specified.

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 reported as 0%, so the description should compensate heavily for 14 top-level parameters. It adds meaning only for top_k, seed, shuffle_labels, and context_structures. Critical parameters such as labels, filters, render, strategy, view, hotspot_zones, include_base64, and output_dir receive no explanation in the description.

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 states a specific composite action: find a slice where all labeled structures are visible and then render it. This clearly identifies the tool as a labeled-image generator rather than a pure slice finder or renderer. It stops short of naming or distinguishing sibling tools such as find_slices, render_image, or batch_labeled_images.

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 explains how to make repeated calls vary or be reproducible via top_k and seed, and how shuffle_labels changes the answer's number. However, it gives no explicit guidance on when to use this tool versus find_slices, render_image, slice_contents, or batch_labeled_images. The usage context is implied by the find-then-render behavior, but alternatives are not addressed.

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