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

With no annotations provided, the description carries the full burden and does disclose key behaviors: substring cell matching, case-insensitivity, and the 50-row upper bound. It does not mention ordering, default limit behavior, or empty-result handling, but these are minor for correct invocation.

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 sentence with no filler, front-loading the result type and core matching behavior before the limit. Every part earns its place.

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 query-and-limit tool, the description plus schema provides enough to invoke it correctly: query is required, limit is optional with min/max, and matching behavior is clear. It does not specify row structure or ordering, but those are not essential for selecting and calling this tool.

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 the query parameter, while limit lacks description. The tool description adds useful semantics for query ('cells contain', case-insensitive), but its 'up to 50' largely repeats the schema's maximum constraint and does not clarify the default limit or behavior 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?

The description specifies the exact behavior: it returns rows of the FindAgency HQ dataset whose cells contain the query, with case-insensitive matching and a 50-row cap. This clearly differentiates it from siblings like dataset_columns, dataset_stats, and dataset_row as the free-text search tool.

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 usage when an agent needs rows matching a substring in any cell, but it does not explicitly state when to use or avoid this tool, nor does it mention any alternatives. Usage is inferable but not clearly guided.

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