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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: it reads only data Parse already holds, and it discloses the shape of the result (where AI names the brand, in which markets, with the data's date) plus a signup link. It stops short of noting freshness limits or cost.

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?

Front-loads the trigger, then input, then output, then the next step, in four tight sentences with no filler. Every clause adds routing or behavioral information.

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 one-parameter, read-only lookup with an output schema, the description covers trigger, accepted input, nature of the return, and the logical follow-up call. Nothing an agent needs to invoke it correctly is missing.

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% with a single documented 'brand' parameter, so the schema already carries the semantics. The description only restates that it 'takes a brand name or website,' adding nothing about normalization or ambiguity handling. Baseline 3 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?

States a specific verb and resource ('Check how AI sees a brand' / returns the brand's public standing from Parse's index) and explicitly separates it from the closest sibling by naming parse_get_brand as the follow-up for questions and sources. An agent can distinguish it from the long list of parse_dashboard_* tools without opening a schema.

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?

Gives a concrete trigger ('Use when someone asks how their brand shows up in AI, or how to show up') and an explicit routing next step ('Next: parse_get_brand with the returned slug'). It does not state exclusions or when NOT to use it versus other standing/overview tools, so it stops just short of a 5.

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