Used-car listing counts per city

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds value beyond them by disclosing the execution model — one request per city, 9 issued together, hence slower than kn_cars — and by describing the return payload (counts plus each city's cheapest ad) in the absence of an output schema.

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?

Three short blocks, front-loaded with the verb and scope, then usage, then the cost caveat. Nearly every sentence earns its place. Minor weakness: '9, run together' introduces an unexplained figure — the city count — that a reader must infer.

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 correctly compensates by naming both return elements (per-city counts and cheapest ad) and warns about latency, which an agent needs for planning. Small gaps remain: whether cities with zero matching ads are included, and what the magic number 9 refers to.

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%, so brand, model and max_price are already documented with formats and units (Toman, English or Persian). The description only restates that these filters are optional and calls max_price a 'price ceiling', adding little beyond the schema. Baseline 3 is appropriate.

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 ('count used-car ads') plus the aggregation unit ('in every city') and the optional narrowing ('one brand, model or price ceiling'). It also discloses a secondary payload, each city's cheapest ad, which no sibling tool provides, so an agent can distinguish it from kn_cars and kn_price_list 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 an explicit use case ('Use to see where a car is for sale') and routes the agent to the right alternative for per-city drilling: kn_cars(city=...). It also flags that it is slower than kn_cars, which is a real selection criterion. It stops short of stating exclusions, e.g. that it should not be used when individual listings are needed, but the routing guidance is clear.

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