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

No annotations are present, so the description carries the full disclosure burden. It discloses useful behavioral traits: case-insensitive matching, substring containment in any cell, and a 50-row cap. However, it omits other behavioral context such as the default limit when none is passed, result ordering, and behavior on 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 14-word sentence with zero filler. Every element — subject, matching semantics, case behavior, and row cap — carries information, and the most important facts are front-loaded.

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 or annotations, the description covers the core semantics well. Gaps remain: whether 50 is the default when limit is omitted, whether results are ordered, and the exact shape of returned rows.

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 already described as 'text to look for in any cell', and the description adds case-insensitive matching and the 'up to 50' cap, which clarifies the limit parameter's effect. The limit parameter still lacks a direct description, but its schema constraints (min 1, max 50) combined with the description's cap statement make its role reasonably clear.

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 specifies a concrete operation: retrieving rows of the Kbasevo dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. It is clear about the resource and semantics, and is conceptually distinct from siblings like dataset_stats, dataset_columns, and dataset_provenance, though it does not name them 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?

No guidance is given on when to choose dataset_search over its siblings or when not to use it. The name and semantics imply it is the full-text row-filter tool among the siblings, but there is no explicit routing, prerequisite, or exclusion information.

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