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

With no annotations provided, the description carries the behavioral burden. It discloses case-insensitive substring matching and the 50-row cap. It does not mention ordering or exact row shape, but the core search behavior is transparently stated.

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 sentence that includes the target dataset, matching rule, case sensitivity, and result cap. Every phrase earns its place and no redundant words appear.

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 with no output schema, the description is nearly sufficient. It defines the matching semantics and result bound, though it omits ordering and the exact fields returned per row.

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 is documented but limit is not. The description partially compensates by saying matching is case-insensitive and results are capped at 50, which clarifies both parameters. However, it does not specify the default limit or whether limit is optional.

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 specific operation: returning rows of the Corp Tax Calculator dataset whose cells contain a case-insensitive query. It clearly distinguishes this from siblings like dataset_row, dataset_stats, and dataset_top by focusing on cell-content matching.

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 gives clear context for when to use the tool: when you need rows matching a text query across the dataset. It does not explicitly name alternatives or exclusions, but its search-specific behavior is unambiguous enough to route an agent correctly.

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