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

Annotations already cover read-only, idempotent, non-destructive, closed-world behavior, and the description adds genuinely new context: the one-decimal star maths, the level thresholds (5 at 10 jobs, 10 at 25, 25 at 75), and that `spoken` is a read-aloud paragraph. It omits edge behavior such as what is returned when there are no reviews yet.

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-loaded with the four trigger questions, then a single dense sentence describing the return payload. The enumeration is long but each item is a real output field, so little is wasted.

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?

There is no output schema, so the description must carry the return contract, and it does so item by item (spoken text, score, review count, verified jobs, level, jobs to next level, star breakdown, unreviewed customers, newest three reviews). For a single-parameter read tool this is sufficient to call it correctly.

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?

With one parameter at 100% schema coverage, the schema fully documents trade_key (format, origin from register_trade), so the baseline of 3 applies. The description adds nothing about the parameter itself.

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 the exact questions answered ("what's my score", "what's my level", "how many reviews", "any new reviews") and enumerates the returned payload, which cleanly separates it from sibling review tools like list_reviews or dispute_review.

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?

Maps user phrasings directly to this tool, which is strong when-to-use guidance. It stops short of an explicit exclusion: 'any new reviews' overlaps with list_reviews, and the definition does not say to use list_reviews for the full review history versus the three newest here.

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