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

With no annotations, the description carries the behavioral burden and does add useful traits: case-insensitive substring containment and an upper bound of 50 rows. It does not disclose ordering, result shape, pagination behavior, or default limit behavior, leaving some uncertainty.

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 conveys the core behavior with no filler. Every element earns its place: what is searched, matching semantics, and result cap.

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, the description plus schema covers required usage adequately: the query is required, limit is optional, and matching behavior is stated. No output schema exists, so the lack of detailed return-format information is a minor gap rather than a fatal one.

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 description coverage is only 50%, so the description partially compensates. It clarifies query semantics by adding case-insensitivity and cell containment, and 'up to 50' maps to the limit parameter, but it does not explain default limit behavior or how limit interacts with result ordering.

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 clear verb and resource: searching rows of the RunbookDesk dataset by cell content. It specifies case-insensitive matching and a 50-row cap, which conveys the core function. It does not explicitly contrast with sibling tools like dataset_row or dataset_top, so it loses the top score.

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 the use case: find dataset rows where any cell contains a query string. However, it gives no explicit guidance on when to choose this over siblings such as dataset_row for exact access or dataset_stats for summary views, and it provides no exclusions.

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