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

The description goes well beyond the annotations by disclosing the return format ('Returns the cleaned CSV plus exactly what changed: rows in, rows out, duplicates removed, cells trimmed per column') and an important integrity check ('The row arithmetic is verified in code — if in − removed ≠ out, the response says so'). It also adds contextual details about handling Chinese full-width whitespace and various empty-value representations, which are not present in the annotations.

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 slightly longer than strictly necessary but every sentence adds value. It front-loads the main purpose, then details operations, return behavior, and a use case. The structure is logical and readable, though it could be tightened without losing critical information.

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 tool with 6 parameters, an output schema, and read-only/idempotent annotations, the description is highly complete. It explains what operations are available, what the output reports, and even the self-verification mechanism. The presence of an output schema means it doesn't need to detail return values, but it does anyway, adding a layer of confidence for the agent.

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?

With 100% schema description coverage, the schema already documents each parameter. The description adds extra meaning by illustrating the 'unify_blank' operation with examples (NA / null / - / 无) and by explaining the split_column and wide_to_long operations in a user-oriented way. This enriches the ops parameter beyond the schema's brief listing.

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 a specific verb and resource ('Tidies a spreadsheet export') and enumerates concrete operations (removes duplicate rows, trims whitespace, unifies blank values, drops empty rows/columns, splits columns). This distinguishes it from sibling tools like diff_tables or merge_tables, which focus on comparison or combining rather than cleaning and reshaping.

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 provides a clear use case: 'Use when a CSV came out of Excel or an export and needs cleaning before analysis.' This is explicit and contextual. However, it does not explicitly mention alternatives or when not to use the tool, so it falls just short of the 5-point threshold.

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