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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds genuine value beyond that: it warns the sample rows are fictional and not real data, and notes the response includes the row limit and the matching rule for existing products — behavioral facts the annotations don't carry.

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 dense sentence that front-loads the main payload (the columns) and then enumerates the extras. It is slightly over-stuffed with comma-separated clauses but every element 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?

With no output schema and no parameters, the description must explain what comes back, and it does: columns in template order, sample rows, row limit and matching rule. Only the response shape/format (e.g. a single object vs. a list) is left 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?

Zero parameters, so there is nothing for the description to disambiguate. Baseline 4 applies.

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 a specific resource: the column set that import_products consumes, along with sample rows, row limit and matching rule. It distinguishes itself from the sibling import_products by describing the specification rather than the action itself, though it uses a noun phrase rather than an explicit verb like 'returns'.

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 phrase 'The columns import_products reads' implies the tool should be consulted before calling import_products, but there is no explicit when-to-use statement or exclusion. The agent must infer the workflow relationship.

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