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

No annotations are provided, so the description carries the full burden. It discloses two meaningful behaviors: case-insensitive matching and a 50-row limit. It stops short of describing the exact fields returned, but for a simple read-only search these constraints are the key behavioral facts.

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. It states the resource, the matching behavior, case sensitivity, and the row cap in the tightest useful form.

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 low-complexity tool with one required string and an optional cap, the description covers matching semantics, case sensitivity, and the row limit. The lack of an output schema means the exact return shape is unspecified, but the essential invocation context is present.

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%: query has a description, but limit does not. The description's 'up to 50' gives limit some contextual meaning, but it never explicitly connects the cap to the limit parameter and adds little beyond the schema's maximum constraint.

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 concrete operation: return rows of the HomeCover HQ dataset whose cells contain the query, with case-insensitivity and a cap of 50. This clearly distinguishes it from siblings like dataset_row (single row) or dataset_stats (aggregates).

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 core use case—searching all cells for a text query—is implied by the title and description, but there is no explicit guidance about when to use this tool instead of dataset_row, dataset_top, or dataset_columns. No alternatives or exclusion conditions are mentioned.

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