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

Annotations already declare this as safe, read-only, and idempotent, so the bar for behavioral disclosure is lower. The description adds useful behavioral context by stating the return contents: matching datasets with their filters and an example query, which goes beyond the annotations.

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?

Two sentences with no filler: the first gives the tool's purpose and illustrative examples, and the second states the return value. Every sentence earns its place and the key action is front-loaded.

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 search tool with no output schema, the description covers the input style and the nature of the response. It could be slightly richer by noting that results are limited to DataScoop datasets, but the tool name and overall context make that implicit.

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 description coverage is 100%, so the schema already documents both parameters. The description adds value by showing how the query parameter should be phrased with natural-language examples, but it does not elaborate on the country parameter beyond what the schema provides.

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 verb ('Find') and resource ('which DataScoop dataset answers a need') and clarifies that it is a keyword-based search tool. It also names concrete example queries, distinguishing it from siblings like list_datasets and query_dataset.

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 strongly implies when to use it: when the user has a need expressed in natural keywords rather than knowing a specific dataset. It does not explicitly exclude alternatives like list_datasets for browsing all datasets, so it lacks an explicit when-not-to-use statement.

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