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

Annotations already declare readOnlyHint=true, openWorldHint=false, destructiveHint=false, so safety is covered. The description still adds value by stating what comes back ('列名与类型') in the absence of an output schema, and by framing this as a prerequisite step. It does not cover behavior on large files or pagination, which keeps it from a 5.

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?

Pipe-delimited sections are front-loaded and scannable: purpose, use-when, aliases, returns. The alias block is somewhat bulky but serves real query-matching value; no filler prose.

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-parameter read-only tool this covers purpose, trigger, and return shape, leaving little an agent needs. Missing only large-file/limit behavior and explicit sibling routing.

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?

Only one parameter, file_id, and schema description coverage is 100%, so the schema already carries the meaning. The description adds nothing about what file_id refers to or where it comes from (upload endpoint), so baseline 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?

States a concrete verb and resource: listing variables in an uploaded data file, plus an explicit return of column names and types. An agent can tell this is a column-inventory operation, though it does not explicitly contrast with near neighbors like dataset_profile or dataframe_parse, which also surface file structure.

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 trigger and even a sequencing rule (call it before Table 1 / modeling so real column names are used), which is genuinely actionable. It stops short of naming an alternative or a when-not condition, so routing versus dataset_profile is left to inference.

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