estimate_cell_diameter

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states it returns a dictionary with estimated diameter and confidence, but does not mention side effects, errors, or operational constraints (e.g., whether it reads-only, modifies files, requires GPU specifics). Since annotations are absent, this is a significant gap.

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 with a clear purpose statement, an Args section, and a Returns section. It is concise and front-loaded with the main action. No redundant or verbose text. The docstring style is appropriate and aids readability, earning a high score for structure.

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 4 parameters, no annotations, and an output schema that is not provided (though indicated to exist), the description is not complete. It does not explain how to interpret the returned dictionary (keys, units, confidence meaning), nor does it mention any requirements like cell vs nuclei models or image dimensionality. An agent would likely need to explore sibling tools or test cases to use this effectively.

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?

The schema coverage is 0%, so the description must compensate. It provides one-line descriptions for each parameter, but these are mostly tautological (e.g., 'model_type: Model type to use for estimation') and do not specify valid values, formats, or relationships. For instance, 'channels: Channel specification' is vague, and no default values or examples are given. This is insufficient for an agent to choose correct parameter values confidently.

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 estimates cell diameter using the Cellpose size model. It uses a specific verb and resource, and the purpose is distinct from sibling tools like segmentation or denoising. However, it does not explicitly differentiate from potential overlap with segmentation tools that might also provide size information, so it isn't a perfect 5.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of scenarios where diameter estimation is preferred over segmentation, or any preconditions like image type or size. The agent is left to infer usage from the description alone, which is minimal.

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