pro_market_positionMarket position of a specific listing: segment median, savings vs market. Médiane de prix du segment comparable d'une annonce + écart € / % vs marché (argument de négociation). listing_id = champ id d'un résultat pro_search/pro_deals.
| Name | Required | Description | Default |
|---|---|---|---|
| listing_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds useful behavioral context by stating it computes segment median and savings (€ and %), which goes beyond annotations. However, it doesn't describe the return format or any edge cases, which is acceptable given the simple read-only nature.
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, front-loaded with the core output (segment median, savings vs market) and then a clarifying note about the parameter. Every sentence adds value and there is no redundancy.
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?
For a single-parameter read-only tool without an output schema, the description covers the purpose, the parameter source, and the type of outputs (median price, difference in € and %). It could elaborate on the exact response structure, but the given info is sufficient for a straightforward query 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?
The input schema only defines listing_id as a string with 0% description coverage, but the description compensates by explaining that listing_id is the `id` field from pro_search/pro_deals results. This gives clear provenance and meaning to the parameter, which is valuable for correct invocation.
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 specifies the tool's purpose: to provide market position for a specific listing, including segment median price and savings vs market. It distinguishes from siblings by focusing on a single listing rather than general market stats or search results, and references the source listing_id from pro_search/pro_deals.
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 context on when to use this tool: when you have a listing_id from pro_search or pro_deals, and you want a negotiation argument. It doesn't explicitly name alternatives to exclude, but the reference to sibling tools provides enough context for an AI agent to understand it's the per-listing market analysis tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.