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

With no annotations, the description carries the behavioral disclosure burden. It reveals case-insensitive substring matching, cell-level coverage, and a maximum result count of 50. It does not disclose ordering, pagination, default limit, or output shape, but the core behavior is unambiguous.

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?

The description is a single 15-word sentence that front-loads the operation ('Rows of the Subsroom dataset whose cells contain the query'). Every word earns its place, with no filler.

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, the description covers the core operation and limit. However, with no output schema and no annotations, it leaves gaps around default limit behavior, result ordering, and the exact structure of returned rows. These are not critical but would improve completeness.

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 schema describes only query, leaving limit to min/max constraints. The description adds case-insensitive matching semantics to the query and interprets the limit as a 50-row cap, which helps the agent understand the parameters' roles. It stops short of stating the default limit when limit is 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?

The description states exactly what the tool does: return rows of the Subsroom dataset where any cell contains the query, with case-insensitive matching and a 50-row cap. This clearly distinguishes it from siblings like dataset_stats (aggregation), dataset_top (ranked rows), and dataset_row (single row fetch).

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 explicit guidance is given about when to use this vs alternatives, nor when not to use it. The purpose implies it is for arbitrary text search across rows, but the description does not mention sibling tools or prerequisites.

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