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

With no annotations, the description must carry the full behavioral burden. It usefully discloses case-insensitivity and the 50-row cap (implying result truncation), but says nothing about permissions, pagination beyond the cap, or ordering of results.

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 zero filler; matching scope, case behavior, and result cap are all stated economically.

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 simple read-only search with no output schema, the description tells the agent what is returned only in the abstract ('rows'). It never describes the shape/fields of a returned row, which is a gap given no output schema exists.

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 is documented in the schema, limit is not but its 1-50 bounds are constrained. The description reinforces the cap ('up to 50') and adds case-insensitivity for query, but gives no guidance on what the limit means in practice (ordering/tie-breaking).

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?

Clear verb (search) and resource (rows of the Tide Times Compare dataset) with a specific matching rule: cells containing the query, case-insensitive. It distinguishes itself from dataset_row/dataset_top implicitly via substring matching, but never names a sibling, so an agent must infer differentiation.

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?

Usage is implied by the contains-match semantics: use it to find rows matching text. No explicit when-to-use versus dataset_top, dataset_row, or dataset_compare, and no exclusions or prerequisites are stated.

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