deblur_image

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

With no annotations provided, the description must fully disclose behavioral aspects. It explains the operation (deblurring) but omits critical details such as whether the input image is modified, the nature of the output dictionary, error handling, or any side effects. The phrase 'Returns dictionary with deblurring results' is vague and doesn't specify keys or structure. This is a significant gap for a tool with no annotation support.

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-organized with an Args section and a Returns section. It is concise, with no filler, and the main purpose is front-loaded. It is not overly verbose for six parameters, and the structure aids readability. Slight deduction for repeating parameter names in the documentation format, which is standard.

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, multiple model choices), the description is inadequate. It does not explain the difference between deblur_cyto2 and deblur_cyto3 models, the meaning of 'channels' in this context, or how to interpret the returned dictionary. While it has an output schema (per context signals), its content is not repeated in the description. The tool is not fully usable by an agent without guesswork.

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 does: each parameter is listed with a brief but meaningful explanation (e.g., 'model_type: Restoration model type (deblur_cyto2, deblur_cyto3, etc.)' and 'diameter: Expected object diameter for scaling'). It adds value by clarifying the purpose of each parameter, even if it doesn't fully explain the 'etc.' or exact formats.

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 'Deblur a microscopy image using Cellpose restoration models', which is a specific verb and resource. It differentiates from siblings like denoise_image and upsample_image by using the term 'deblur', though it doesn't explicitly contrast with them. The purpose is clear and unambiguous for an agent.

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 mention when to use this tool versus alternatives such as denoise_image or restore_and_segment. It provides no context on selecting between deblurring and other restoration methods, nor does it explain prerequisites or conditions. An agent would have to infer usage from the tool name and model_type parameter.

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