CarHunt

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.

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

Annotations only cover readOnly/openWorld, but the description adds real behavioral context: per-IP and per-plan rate limits (3/day anonymous, 15/30/unlimited with keys) and the fallback behavior when mileage is omitted (uses median comparable mileage and flags it in `note`). That is meaningful disclosure beyond the structured hints, though it omits anything about comparison freshness or error cases.

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 purpose followed by output description, quota rules, and mileage semantics. It is a bit dense and mixes English and French, but each sentence contributes usable information with no filler.

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 read-only estimation tool with no output schema, the description covers purpose, output fields, quota constraints, and the key mileage-omission behavior. It is largely self-sufficient; the main miss is any explicit routing versus the sibling search/stats tools.

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 baseline is 3, but the description adds genuine meaning beyond the schema: it explains that mileage is a second-order price factor, that omitting it triggers a median-mileage fallback reported via `note`, and it names the returned price tiers. This compensates well for what schema alone would carry.

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 (estimate) and resource (market price of a used car in France), and spells out the outputs (Q1/median/Q3, confidence index, comparable listings). It is unambiguous about what it returns, though it does not explicitly differentiate itself from siblings like car_market_stats or search_used_cars.

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?

Usage is implied rather than stated: it explains that mileage is optional and describes quota tiers, but never says when to pick this tool over search_used_cars or car_market_stats. Rate-limit context is present, but no explicit when-to-use/when-not guidance or named alternatives.

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

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

Beyond the readOnlyHint annotation, the description adds the default min_deal_score=60 threshold, the sorting direction (most to least underpriced), and the API key requirement. It does not conflict with annotations; no return format is described, but annotations already cover the safety profile.

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 short and front-loaded with the core function, but it mixes languages (English then French) and slightly restates the concept ('Les meilleures affaires du marché'). Still, it is efficient and each sentence adds something.

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?

Despite having 20 parameters, the high schema coverage and read-only annotation allow the description to remain high-level. It provides the feed's purpose, sorting, and auth requirement. It does not explain return values or pagination, but the schema covers parameter details and the output is a simple list feed.

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 90%, so the schema already documents parameters well. The description adds no new parameter meaning beyond what schema provides; it mentions min_deal_score default which is already in the schema. Thus 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?

The description clearly identifies this as an underpriced car deals feed, sorted by deal-score, and distinguishes it from general search tools by emphasizing it surfaces deals below market median ('nettement sous la médiane') and positions it as 'L'outil de pige des négociants' (dealer benchmarking tool). This is a specific verb+resource with clear differentiation from siblings.

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?

It provides clear context for when to use (finding underpriced deals, dealer benchmarking) and notes a prerequisite (Business API key). However, it does not explicitly name alternatives like pro_search or search_used_cars or state when not to use it, so it stops short of full exclusions.

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

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.

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

Beyond the readOnlyHint annotation, the description discloses critical side-effects: it 'Consomme le quota mensuel de véhicules de l'organisation' and requires a paid API key. It also reveals behavioral details like incremental polling (since) and the 50-results-per-page cap. This goes well beyond the annotations.

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 compact, two sentences, with the key value proposition upfront. Every clause adds information: features, filtering options, pagination, polling, authentication, and quota impact. No redundant wording.

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's complexity (21 parameters, no output schema), the description covers the essential behavioral aspects: deal-score, filters, pagination, incremental polling, auth, and quota. It does not explicitly describe the response structure, but since it is a search tool, the return shape is partly inferable. The description is sufficient for an agent to select and use the tool 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 86% schema description coverage, the schema already documents most parameters individually. The description adds meaningful context by linking parameters to the deal-score concept (min_deal_score, sort) and explaining incremental polling (since). It also clarifies pagination behavior (limit max 50). A few undocumented parameters remain, but the high schema coverage keeps this at a strong score.

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's purpose: 'Full professional search with deal-score, 50 results/page, incremental polling.' It specifies the resource (search), target audience (professionals), and distinct features (deal-score, polling) that differentiate it from basic search tools. The scope is precise and actionable.

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 provides contextual prerequisites (requires CarHunt Pro API key, consumes monthly quota) but does not explicitly state when to use this tool versus alternatives like search_used_cars or pro_deals. Usage is implied for professional searches needing deal-scores, but no exclusions or alternatives are mentioned.

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

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

The readOnlyHint annotation already indicates this is a safe read operation. The description adds some context by specifying the scope (current month) and the metrics included, but it does not disclose additional behavioral traits such as authentication requirements, timezone details, or the meaning of 'extra credits'.

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 concise, consisting of two short sentences. However, the second sentence is a direct French translation of the first, repeating the same information, which is slightly redundant but still brief and front-loaded.

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 parameterless, read-only tool with no output schema, the description is adequate. It clearly states the purpose and key data elements. It could be more complete by explaining terms like 'extra credits' or the exact period definition, but overall it provides sufficient context.

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 zero parameters, the baseline is 4. The description adds value by enumerating the output fields (quota, consumed, remaining, extra credits), which helps the agent understand what to expect in the response even without an output schema.

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's purpose: reporting current month API usage with specific data points (quota, vehicles consumed, remaining, extra credits). This is specific enough to distinguish it from sibling tools like pro_search or car_market_stats, which focus on different operations.

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 usage by its name and content, but does not explicitly state when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. There is no direct guidance on when this tool is appropriate.

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

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

Annotations only declare readOnlyHint=false and openWorldHint=false. The description adds meaningful behavioral context: periodic evaluation (every 10 minutes), webhook delivery via POST JSON, and quota consumption. It does not contradict annotations and provides useful operational details beyond the structured fields.

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 brief (two sentences, one English and one French) and front-loads the main purpose and behavior. The bilingual repetition is slightly redundant but not wasteful. It earns a 4 for being compact and structured.

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's complexity (17 parameters, no output schema), the description covers the essential workflow: creation, evaluation interval, webhook delivery, quota impact, and authentication requirement. It does not explain return values or error handling, but these are partially covered by the absence of an output schema. Overall, it is sufficiently complete for an agent 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?

Schema description coverage is 65%, so the schema already documents most parameters. The description mentions webhook_url and criteria in general but does not add detail for undocumented parameters like name, finition, or motorization. It adds minimal value beyond the schema, so a baseline of 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?

The description clearly states the verb ('Create a watchlist') and the resource, and explains the core behavior: criteria evaluated every 10 minutes and matching listings pushed to a webhook. It is distinct from sibling tools like pro_watchlist_delete and pro_watchlist_list, making its purpose unambiguous.

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?

No explicit guidance on when to use this tool versus alternatives such as pro_search or search_used_cars. It does state a prerequisite (API key with write scope), but does not mention when not to use it or how it compares to other search/watchlist tools.

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

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

The description simply repeats the delete action without disclosing behavioral traits beyond the annotations. It does not mention irreversibility, ownership requirements, or any side effects. The title mentions 'clé API, scope write' but the description omits this, leaving out important context about authorization.

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 short and front-loaded with the primary English sentence. The subsequent French sentence is a direct translation and somewhat redundant, but it's still concise and doesn't waste much space.

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 destructive operation with no output schema, the description is incomplete. It doesn't mention return values, error conditions, or prerequisites such as authentication scope. The reference to a sibling tool adds minimal context. Overall, it's not enough to fully guide invocation.

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 0%, so the description must compensate. It only says 'by id' which adds minimal meaning about the watchlist_id parameter. It doesn't explain what the id is, where to find it (e.g., from pro_watchlist_list), or any constraints on its format.

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 'Delete a watchlist by id.' This specifies the action (delete), the resource (watchlist), and how it's identified (by id). It distinguishes itself from sibling tools like pro_watchlist_create and pro_watchlist_list by making the deletion action explicit.

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 provides no guidance on when to use this tool versus alternatives. The 'cf. pro_watchlist_list' reference merely points to a related tool but doesn't serve as a usage guideline or exclusion. No prerequisites or conditions for deletion are mentioned.

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

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

Annotations already mark the tool as read-only, so the safety profile is covered. The description adds useful behavioral context: watchlists are evaluated every 10 minutes and can notify via email, Discord, or webhook JSON, which clarifies what the listed objects represent. It does not cover response format or pagination, but those are less critical here.

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 with no filler: the first states the action and resource, the second adds relevant context about how watchlists work. 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?

For a zero-parameter, read-only list operation with annotations covering safety, the description is sufficient. It explains what watchlists are and implies the return is a list of them; no output schema is present, but the tool is simple enough that nothing essential is missing.

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 tool has zero parameters, so the schema is trivially complete and the description correctly adds no parameter details. Baseline 4 applies because there is no parameter burden to compensate for.

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 uses a specific verb ('List') and a specific resource ('the organization's watchlists'), and further clarifies that watchlists are automatic alerts. This makes it easy to distinguish from the sibling create/delete watchlist tools even without naming them.

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?

No guidance is given about when to use this tool versus the sibling pro_watchlist_create or pro_watchlist_delete tools. The usage context is only implied by the verb 'List' and the resource name, with no explicit conditions or alternatives.

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

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

Annotations already state readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds auth requirements (Bearer chu_live_…), result-count limits per tier (10 vs 25), the deal-score gating by tier, and the presence of source_url in each listing with a citation instruction — all behavior beyond the structured fields.

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?

It is front-loaded with the core purpose, then the aggregation sources, then access tiers, then the source_url citation rule — a logical ordering. The bilingual EN/FR duplication adds some length without new meaning, keeping it just below top marks.

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 partially compensates by noting that listings carry source_url and deal-score. It covers auth, limits, and aggregation scope, so an agent has enough to call it correctly; only the sibling-routing detail is absent.

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 93%, so parameters such as model, region, and sort are already well documented in the schema. The description adds tier context around limit and deal_score, but does not add syntax or format meaning beyond what the schema already provides, making the baseline 3 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?

The description names a specific verb and resource ('Search used cars for sale in France'), names the aggregation sources (CarHunt over LeBonCoin, LaCentrale, L'Argus…), and scopes the free vs. keyed tiers. An agent immediately knows this is a multi-criteria aggregated search, distinguishable from single-valuation siblings like estimate_car_price.

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?

It clearly conditions behavior: without a key, 10 results and no deal-score; with a Premium/Pro/Expert key, 25 results plus deal-score and deal_score sorting. That is concrete when-to-use guidance tied to access level. It stops short of explicitly routing between this and sibling tools such as pro_search or pro_deals.

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