upsample_image

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions GPU acceleration and optional output path but does not disclose prerequisites, potential side effects (e.g., file writing), or the structure of the returned dictionary. The description is sparse on details like error handling, model availability, or whether the operation is read-only, leaving significant ambiguity.

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 a well-structured docstring with clear Args and Returns sections. It is not overly verbose and front-loads the purpose. Minor redundancy exists (e.g., repeating 'upsampling' in multiple places), but it remains concise and readable.

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 (6 parameters, no annotations, and no explicit output schema details), the description is incomplete. It lacks critical context such as the exact format of the return dictionary, how to choose among model_types, the expected channels format, and any prerequisites or limitations. An agent may struggle to use this tool correctly without additional information.

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 0%, so the description must compensate. It provides useful hints for some parameters (e.g., scale_factor 'typically 2 or 4', model_type examples) but leaves others vague (e.g., channels: 'Channel specification' without format or allowed values). It adds some value over the bare schema but does not fully clarify all six parameters.

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 a specific action (upsample) on a resource (microscopy image) using Cellpose restoration models. It distinguishes from sibling tools like denoise_image and deblur_image by its focus on upsampling, making it clear what this tool does and how it differs.

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 does not provide any guidance on when to use this tool versus alternatives. It does not mention any conditions, scenarios, or comparisons to sibling tools. An agent would have to infer usage from the name and general knowledge, as no explicit selection criteria are provided.

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