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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, yet the description still adds real value: the exact return shape (15 rows × 113 columns), determinism ('Always the same rows'), and the absence of an auth requirement. It omits rate limits or pagination, but for a static preview tool that is minor.

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 essential facts are front-loaded, but the sentence is padded with marketing and provenance detail ('the same sample as on Databricks Marketplace', a marketing URL, repeated restatement of 500 companies/113 columns, and 'Free' said twice). Roughly a third of the text does not help an agent invoke the tool.

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?

With no parameters and no output schema, the description carries the return-shape burden and does so: row count, column count, ordering, determinism, sample population, auth model, and where to get the full file. Only the sibling-routing gap keeps it from being fully complete.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. It correctly signals that no filtering, row-count, or format arguments exist, which prevents an agent from attempting to pass them.

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?

States a concrete verb and resource ('Sample rows from the Engagemii dataset') plus precise scope: the first 15 rows, 500 US companies, all 113 columns in purchased-file order. An agent can tell exactly what comes back, though it never names which sibling (data_records, data_orders, kit_download) it supersedes.

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?

It gives a routing hint for the full dataset (download at engagemii.com/aeo/data after a work email) and notes the tool is free and auth-free, which implies 'use this for a quick preview'. But it never states when to prefer a sibling tool such as data_records or data_signup, so sibling selection is left to inference.

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