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

With no annotations, the description carries the behavioral burden. It discloses important traits: substring containment, case-insensitivity, and a 50-row maximum. However, it does not mention default limit behavior, result ordering, or what happens when no rows match, which would be useful in the absence of annotations.

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, dense sentence conveys the resource, matching rule, case sensitivity, and row cap with no filler or redundancy. It front-loads the core behavior and earns every word.

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 search tool, the description covers the essential semantics needed to call it correctly. It is slightly incomplete in not specifying the default row limit or result ordering, but the return value is clearly described as rows and the input schema covers the two parameters.

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?

The description adds value by clarifying case-insensitive matching and the 50-row cap, but the schema already documents the query as text to look for in any cell. The optional limit parameter still lacks a description in the schema, and the description does not explain its default value or effect 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 resource (Sopvo dataset) and a precise matching behavior: rows whose cells contain the query, case-insensitively, up to 50. This clearly differentiates dataset_search from siblings like dataset_columns, dataset_stats, and dataset_top, which serve different purposes.

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 implies when to use the tool—when you need row-level results matching text in any cell—but it provides no explicit guidance on when to prefer alternatives or exclude other tools. There is no mention of cases where dataset_row, dataset_stats, or dataset_top would be more appropriate.

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