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

With no annotations, the description must carry the behavioral burden. It usefully discloses case-insensitive matching and the 50-row result cap, but says nothing about ordering, whether results can be paged past 50, or performance on large datasets.

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?

A single front-loaded sentence with no filler; the resource, matching rule, and cap are all stated compactly.

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?

No output schema and no annotations, so the description is the only source of truth, yet it never describes the shape of a returned row or the ordering of results. Adequate for basic invocation but leaves an agent guessing at the return payload.

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 coverage is 50% (query documented, limit undocumented beyond its maximum). The description adds that the query matches any cell case-insensitively and that results cap at 50, which aligns with and slightly extends the schema, but it never states the limit's default or behavior when omitted.

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 verb and resource — returning rows of the named 'Med Spa Cost Checker' dataset that match a query, with case-insensitive substring semantics. That is clear, but it offers no differentiation from many sibling tools (dataset_row, dataset_top, dataset_columns) that an agent must choose between.

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 explains the matching mechanism but never says when to reach for this tool instead of dataset_row, dataset_top, dataset_stats, or dataset_columns. No prerequisites, exclusions, or alternative routing are provided.

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