check_fair_price

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

No annotations are provided, so the description carries the full burden. It transparently discloses the output behavior: a price range built from real listings, an optional plain verdict, and the canonical-format requirements for make/model. It does not mention potential failure modes or data freshness, but the disclosure is strong for a read-only comparison tool.

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 description is front-loaded with the core purpose, then explains output, scope, and parameter formatting in a logical order. Every sentence adds value; examples are concrete and the IMPORTANT caution is placed near the end without bloating the text.

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?

Given the tool has no output schema, the description adequately covers return semantics (low/typical/high range and fair/above/below verdict). It also handles the tricky naming prerequisite. It stops short of describing exact output structure or error behavior for unknown or misspelled models, but these are minor gaps for this query-style tool.

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 100%, so the baseline is 3. The description adds meaningful semantics beyond the schema by explaining why asking_price is passed, and by emphasizing canonical Latin make/model formats with concrete examples ('Renault' not 'VW', 'Clio' not 'Clio dCi'). This materially helps the agent invoke the tool correctly.

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 clear verb-resource pair: 'Check whether a car's asking price is fair for the Portuguese market.' It further specifies the inputs (make, model, year, mileage), what is returned (low/typical/high range plus a verdict), and distinguishes itself from inventory tools by noting it works for ANY car, including listings found elsewhere.

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 description gives clear usage context—when evaluating a car's price before purchase—and explicitly directs the agent to call list_makes_models first if unsure of spelling. It does not explicitly enumerate exclusions for sibling tools like search_inventory or get_car, but the 'ANY car' statement provides enough distinction.

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