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

No annotations are provided, so the description carries the full burden. It usefully discloses case-insensitive matching and a 'up to 50' result cap, which frames it as a bounded read, but it is silent on the default limit, how truncated results are signaled, error behavior, and whether any permissions are needed.

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 compact sentence that front-loads the resource and operation and packs scope, matching semantics, and result cap with no wasted words.

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?

With no output schema, the description does enough to say rows are returned, but it omits the row/column shape of results, the default limit value, and truncation signaling. For a two-parameter search tool with no annotations, these are meaningful gaps rather than fatal ones.

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 50%: 'query' is already documented in the schema, while 'limit' is not. The description's 'up to 50' loosely reflects the limit maximum but never ties it to the limit parameter, states its default, or explains how the two interact, so it only partially compensates for the gap.

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 specific operation (search) against a specific resource (the Capital Gains Tax HQ dataset) and states what is returned (rows whose cells contain the query, case-insensitive). It does not explicitly distinguish itself from siblings like dataset_row or dataset_top, but the verb+scope are clear enough that an agent can identify it as the full-text search tool.

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?

There is no when-to-use or when-not-to-use guidance and no alternatives named. The agent must infer that this is the tool for ad-hoc text lookup versus dataset_row (fetch a row) or dataset_stats, with no help from the description.

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