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

With no annotations, the description carries the full behavioral burden. It does disclose important behavior: case-insensitive matching and a 50-row maximum. However, it does not mention whether the tool is read-only, what the default limit is when omitted, or what the returned rows look like.

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 sentence with no filler. The core result is front-loaded, and every clause adds useful information about matching behavior or result limits.

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 two-parameter search tool with no output schema, the description provides the essential behavior an agent needs to call it: what is searched, how matching works, and the row cap. Minor gaps remain around the default limit and the exact shape of returned rows, but these are not critical for invocation.

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 only 50% because the limit parameter lacks a description. The description compensates by clarifying that the query is matched case-insensitively in any cell and that results are capped at 50, adding meaning beyond the schema's bare constraints.

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 states a specific verb and resource: it returns rows of the Termslane dataset matching a query in any cell. It also adds the key scoping details—case-insensitive matching and a 50-row cap—which distinguish it from sibling tools like dataset_stats or dataset_top.

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 intended use is implied: use this tool when you need rows whose cells contain a given text. However, it does not explicitly say when to prefer it over sibling tools or when not to use it, so the agent must infer the decision from the sibling names alone.

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