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 a hard result cap of 50, which are real behavioral traits, but says nothing about permissions, whether match ordering is deterministic, or what happens with zero matches.

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: resource, match semantics, and result cap in one pass. Nothing could be removed without losing 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 search tool with no output schema, the definition is minimally viable but leaves the row shape and result ordering unexplained. The two-parameter surface is simple enough that the gaps are tolerable, but a search tool benefits from stating what a returned row contains.

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% (the limit parameter has no schema description), and the description compensates by explaining that limit means 'up to 50' and that query matches any cell case-insensitively. It adds genuine meaning beyond the schema for the undocumented parameter, though it omits the minimum of 1.

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 verb (search), the exact resource (rows of the Roofing Quotes UK dataset), the matching rule (cells contain the query, case-insensitive), and the cap (up to 50). It clearly distinguishes from siblings like dataset_row or dataset_top by being a text-match search, though it never names those alternatives explicitly.

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 only implied: an agent can infer this is the tool for locating rows by text content. There is no statement of when to prefer it over dataset_top, dataset_compare, or dataset_row, and no prerequisites or exclusions are given.

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