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

Despite having no annotations, the description adds useful behavioral context: the output artifact is written to the file transfer station ('产物落文件中转站'), the tool returns cleaning statistics, and it costs 1 credit per call. This covers output destination, return value type, and a rate/cost limitation, though it does not mention auth requirements or whether the source is left untouched.

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 a single, front-loaded sentence that packs the core purpose, high-level operations, output location, return value, and cost. Every phrase earns its place with no redundancy or filler.

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?

Given 8 parameters, a rich schema with 100% coverage, and no output schema, the description provides sufficient high-level context: it names the main cleaning operations, explains where results go, and notes the returned statistics. It doesn't explicitly state that file_id or data_base64 are mutually required, but the schema already notes this, so the description covers what's needed for a quick understanding.

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 parameters are fully documented in the input schema. The description's mention of operations like dedupe and trim aligns with specific boolean parameters, but it does not add new semantic meaning beyond that association. The schema already explains each parameter's default and purpose.

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 clearly states the tool's purpose with specific verbs and resources: '清洗脏数据' (clean dirty data) and lists concrete operations (去重、去空白、删全空行列). This distinguishes it from sibling tools like data_convert, data_inspect, data_join, data_profile, and data_query, which handle other transformation tasks.

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 description does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among the sibling tools. It implies usage for cleaning messy data but lacks clear direction on when to prefer this tool over data_convert or data_profile.

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