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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds little behavioral context beyond saying 'automatically' and repeats the return label '细胞类型注释' without explaining the annotation method, limitations, or output structure.

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 short and front-loads the usage condition, but it is formatted as keyword metadata rather than natural guidance. The ALIASES list and the redundant 'RETURNS: 细胞类型注释' add little value.

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 single-cell annotation tool with no output schema, the description should clarify what the annotation result looks like or what method is used. Instead, 'RETURNS: 细胞类型注释' merely repeats the tool's purpose, leaving return semantics and method expectations underspecified.

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 input schema has 100% description coverage and thoroughly documents the single data_json parameter, including its required shape. The description adds no parameter semantics beyond what the schema already provides, so the baseline of 3 applies.

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 verb and resource: automatically annotate cell types in single-cell clusters. This is clear enough to distinguish it from general clustering or marker detection, though it does not explicitly name alternatives like scrna_cluster or scrna_markers.

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 'USE WHEN' clause gives a clear usage context: automatically annotating cell types in single-cell clusters. It does not provide when-not-to-use guidance or name alternative sibling tools, but the intended context is explicit.

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