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 only 'RETURNS: 描述统计与质量概览', with no mention of cost, runtime, sampling behavior on large files, or whether extra columns are written — modest added value over the structured data.

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 tag format is front-loaded with the trigger condition and has no filler prose. The alias list is long by nature but earns its place for retrieval matching, so the only weakness is density rather than waste.

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 two-parameter, read-only tool with no output schema, the description covers the intent and the returned summary's nature, which is sufficient to call it correctly. It leaves open where an agent obtains a valid file_id, but that is a minor gap given openWorldHint=false and full schema coverage.

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%, with file_id ('数据文件 id') and study_design ('研究设计') both documented, so the baseline is 3. The description adds no new meaning about how study_design changes the profile output, leaving the schema to do all the work.

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 names a concrete verb+resource ('first look at a data file') and enumerates the outputs: distributions, missingness, outliers, data quality. That is clearly distinguishable from neighbors like dataframe_parse (parsing) or dataset_columns (column listing), though it never names those siblings explicitly.

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: first look at a data file' trigger gives a clear entry condition, and the alias list further anchors the intent in both Chinese and English. It stops short of stating when NOT to use it or which sibling to prefer for deeper analysis, so it is strong context without explicit routing.

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