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

With no annotations, the description carries the disclosure burden. It adds case-insensitive matching and the 50-row cap, both useful, but it does not specify default limit behavior, ordering, or what happens when a query matches no rows.

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. The resource and operation are front-loaded, and every word 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 two-parameter read-only search tool, the description covers the core invocation information: matching behavior, case sensitivity, resource, and row cap. Missing output structure and ordering details are secondary for this simple 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 already describes query as 'text to look for in any cell,' and the description adds case-insensitivity and clarifies that the limit caps returned rows at 50. This goes beyond the raw integer type and maximum in the schema.

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 the specific operation, resource, and matching semantics: rows of the Yearendo dataset whose cells contain the query. The case-insensitive and 50-row qualifiers distinguish it from sibling tools like dataset_row and dataset_stats.

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 intended use is clear: call this when you need content-based row retrieval across all cells. It does not explicitly name alternatives or exclusions, but the context is unambiguous enough for an agent to select it appropriately.

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