car_market_statsMarket statistics for a used car model in France (median price, volume). Médiane de prix du segment (année ±1, tranche de km) et volume d'annonces actives pour un modèle sur le marché français.
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | ||
| year | No | Année du segment (optionnel, ±1 an) | |
| model | Yes | ||
| mileage | No | Kilométrage du segment (optionnel, tranche ±20k) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the read-only nature is covered. The description adds context about what statistics are included (median price, volume) and how they are segmented (year ±1, mileage range), which goes beyond the annotation. However, it does not disclose return format, pagination, or any rate limits. Given annotation coverage, a score of 3 is appropriate.
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 two sentences, but they are redundant: the English sentence summarizes the French sentence, which provides additional segment details. The information could be merged into one concise sentence. It is not overly long, but the redundancy means not every sentence earns its place.
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?
The tool has no output schema, so the description should explain what is returned. It mentions 'median price, volume' but does not specify the return structure (e.g., a single object with price and volume fields). Given the complexity of 4 parameters and no output schema, the description is adequate but not complete. It covers the core functionality but lacks details on response format or example usage.
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 50% (year and mileage are documented). The description adds meaning by explaining that year and mileage define a segment (year ±1, mileage ±20k), which helps clarify how these optional parameters affect the statistics. However, make and model are not explained in the description or schema, leaving some ambiguity. The description partially compensates for the coverage gap but not fully.
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 clearly states the tool provides market statistics for a used car model in France, specifying median price and volume. This distinguishes it from sibling tools like search_used_cars or estimate_car_price by focusing on aggregated market data rather than listings or single-price estimation. The resource is specific (model in France), and the verb is implied ('provides' or 'retrieves').
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 implies the tool is for obtaining market statistics for a model, but it does not explicitly state when to use it over alternatives. The title mentions 'gratuit' (free), suggesting it is the free tier compared to pro tools, but the description does not elaborate on exclusions or alternatives. Usage context is clear but not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Tools are mostly distinct, with slight overlap between pro_search and search_used_cars, but descriptions differentiate based on professional features and deal scores. Pro_deals and pro_search also have different purposes.
All tool names use snake_case with a consistent pattern: descriptive nouns for consumer tools (car_market_stats, estimate_car_price, search_used_cars) and pro_ prefix for professional tools (pro_search, pro_deals, pro_watchlist_create). No mixing of conventions.
10 tools is well-scoped for a car market API covering both consumer and professional needs. It is not too few or too many, and each tool serves a clear purpose.
The tool set covers core operations: market stats, price estimation, search, deals, watchlists, and usage. Missing a dedicated tool to retrieve full details of a single listing by ID, but agents can work around using search results.