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 'RETURNS' line adds genuine value by naming shape, column types and missingness as outputs. However, nothing is said about malformed or non-CSV input, size limits, or how types are inferred, which are the real behavioral risks for a parsing tool.

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 definition is a single front-loaded line with labelled sections (purpose, USE WHEN, ALIASES, RETURNS), so the agent can scan it quickly. The alias list is long and repeats the purpose in Chinese and English, which is useful for matching but is the least economical part.

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 one-parameter, read-only, side-effect-free utility with no output schema, the description covers trigger, aliases and return content, which is essentially everything needed to call it correctly. The remaining gap is sibling disambiguation against dataset_profile/dataset_columns.

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?

There is a single parameter with 100% schema description coverage ('CSV 文本'), so the schema carries the burden and baseline is 3. The description adds only the nuance that the data comes from a user paste rather than a file path or dataset id. No format details (delimiter, encoding, header presence) are added beyond the schema.

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 ('Parse pasted data', 'infer column types') plus the concrete outputs it derives ('get shape, column types, missingness'). An agent can tell it is a data-ingestion/typing utility. It does not, however, differentiate itself from close siblings like dataset_profile or dataset_columns, which appear to overlap heavily.

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 concrete trigger: the user pasted a CSV/table blob and the agent needs shape/types/missingness before analysis. That is a clear context for selection. It stops short of naming alternatives or stating when not to use it (e.g., for an already-loaded dataset), so it falls below a 5.

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