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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds useful context that execution is by a real algorithm engine rather than an LLM, and that it returns descriptive statistics and a quality overview, but it does not detail what specific statistics or quality checks are included.

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 two compact sentences with a front-loaded bracketed label, followed by the user need and return value. There is no unnecessary wording; every part contributes to understanding the tool's purpose.

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 simple tool with two parameters and no output schema, the description covers the core purpose, inputs implied, and the nature of the return. It could be slightly more complete by specifying which descriptive statistics or quality dimensions are included, but it is adequate for correct selection and invocation.

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 file_id and study_design are already documented in the schema. The description does not add any parameter-level meaning beyond what the schema provides, making the baseline score appropriate.

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?

States a specific action and resource: a quick data profile that shows what the data looks like, its distribution, and missingness, and returns descriptive statistics plus a quality overview. It is clear what the tool does, but it does not explicitly distinguish itself from siblings such as act_file_columns or act_distribution_fit.

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 phrase '用户想先看看' gives a clear usage context: an initial exploratory look at a dataset. It does not state when not to use it or name alternative tools for deeper analysis, so it lacks explicit exclusions.

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