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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely new behavioral context: the data is derived from real repair records and normalized as a share of diagnosed jobs, and pro_only results carry a safety restriction on DIY advice. It doesn't describe result ordering or how ties/sparse samples are handled.

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 scope and the disclaimers are front-loaded and every clause carries information, but the opening sentence is long and stacks appliance list, brand list, and the share-of-jobs metric into one dense clause. Slightly tighter phrasing would scan faster without losing content.

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, the description does the work of explaining what comes back (findings as a share of diagnosed repair jobs) and flags the pro_only safety dimension. That covers the essentials for a read-only lookup; only fine-grained output details like ordering or minimum sample size are absent.

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 67%, and the description meaningfully extends it: it enumerates the accepted brands (samsung, lg, whirlpool, ge, frigidaire, maytag, kenmore, hisense) that the schema only illustrates with one example, and explains that problem takes the customer's own phrasing and scopes results to that complaint.

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 precise verb+resource: it returns what technicians actually found when diagnosing specific appliances, expressed as a share of diagnosed repair jobs from real repair records. It names the exact appliance set and brand set, and explicitly disclaims what it is not ("It is not a reliability ranking"), which separates it from the pros-oriented siblings.

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 explicit triggering questions ("what usually goes wrong with my Samsung fridge?") and shows the alternative invocation shape when the problem parameter is supplied ("my Samsung fridge is not making ice, what is it usually?"). It also states a clear usage boundary: pro_only findings are gas/240V/sealed-refrigerant jobs and must not be offered as DIY.

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