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

Annotations already cover the safety profile (read-only, non-destructive, closed-world), so the description only needs to add operational context. It adds 'RETURNS: 分割统计结果', which is vague boilerplate rather than a described output, and it never clarifies whether cell_density/positive_rate are inputs to be supplied or results this tool produces — a meaningful ambiguity for a counting tool. It does not contradict annotations.

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 pipe-delimited layout is front-loaded and scannable, with purpose first, then use-when, aliases, and returns. The bilingual duplication and a long alias list (including generic terms like 密度/segmentation) add some noise but the whole thing stays short.

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?

With three optional parameters, no output schema, and rich annotations, the definition is close to sufficient, but it omits the one thing an agent most needs here: what the tool actually consumes and what statistics come back. 'RETURNS: 分割统计结果' does not convey the return shape, and no prerequisite about image/section input is given.

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 baseline is 3 and the schema already names all three parameters. The description adds no format, units, or valid-range guidance for stain_type, cell_density, or positive_rate, and leaves unresolved whether the numeric fields are inputs or expected outputs.

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 gives a specific verb+resource: 'Nuclei segmentation & counting', reinforced by the Chinese title and a use-case tagline about cell density / positive rate from a tissue section. It is distinguishable from most nearby siblings (ihc_hscore, mif_phenotype), though the overlap with quantitative-imaging tools is not explicitly addressed.

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?

'USE WHEN: cell density / positive rate from a tissue section' gives one concrete trigger condition, which is better than nothing. However, there are no exclusions and no named alternatives for closely related siblings like ihc_hscore, mif_phenotype, or tumor_microenvironment, so the agent must infer boundaries.

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