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?

The description discloses methodology (based on comparable real ads), output components, optional mileage behavior (uses median and signals in note), and rate limits. This goes well beyond the readOnlyHint annotation, providing rich behavioral context for the agent.

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 and front-loaded with the main purpose, followed by essential details. Every sentence contributes value—methodology, optional mileage, and rate limits—with no redundancy or fluff.

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 description fully explains what the tool returns (Q1, median, Q3, confidence, similar ads) even without an output schema. It also covers edge cases (missing mileage) and quota behavior, making it complete for agent use.

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 descriptions cover 100% of parameters and already explain each field, including detailed guidance on mileage. The tool description adds no new parameter-specific meaning beyond what the schema provides, so 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 states 'Estimate the market price of a used car in France' with a specific verb and resource. It further details the output (Q1, median, Q3, confidence index, similar ads), distinguishing it from sibling tools like search_used_cars or car_market_stats.

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 on when to use the tool (for pricing a used car) and includes usage limits (3/day without account, quota with key). However, it doesn't explicitly contrast with alternatives or state when not to use it, though sibling tools are clearly different.

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?

Discloses the ~10 minute push delay, that delivered vehicles count against the monthly quota, and requires an API key with write scope. Annotations only state readOnlyHint=false, so this additional context is valuable and non-contradictory.

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?

Two sentences, but the first English sentence and the French sentence repeat the same core idea. Some redundancy, though it is still relatively compact.

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 description covers the core behavior but lacks information about the response format (e.g., whether an ID is returned) and how it relates to delete/list siblings. With 17 parameters and no output schema, more detail on expected return could be helpful.

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 description references webhook_url and the matching criteria concept but doesn't elaborate on specific filter parameters. Schema descriptions cover 65% of parameters, and the gaps (e.g., name, finition, motorization) aren't addressed in the description.

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 'Create a watchlist' and explains the webhook push behavior, clearly identifying the action and resource. It doesn't explicitly reference sibling watchlist tools, but the verb 'create' versus 'delete'/'list' makes the purpose distinct.

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 specifies when to use it (to get pushed notifications for new matching listings) and includes a prerequisite ('Clé API avec scope write requise'). It doesn't mention alternative tools like pro_watchlist_list/delete, but the context is clear.

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?

The annotations already declare readOnlyHint=true, so the description adds value by explaining the nature of watchlists (real-time alerts with ~10 min delay, delivery via email/Discord/webhook). This is useful context beyond the structured annotations, with no contradictions.

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, using two short sentences. However, the French sentence partially repeats the English sentence ('Watchlists de l'organisation' = 'the organization's watchlists'), which is slightly redundant though not overly wasteful.

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 is simple with no parameters and no output schema. The description explains what watchlists are and the listing operation, but it does not specify what the response will contain or any edge cases (e.g., empty list). Given the lack of an output schema, more return-value detail would improve completeness.

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 input schema covers everything. The description does not need to add parameter details, and the baseline score of 4 is appropriate for zero-parameter tools.

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 the specific verb 'List' with the resource 'the organization's watchlists' and adds clarifying context '(real-time alerts)'. It clearly distinguishes this from sibling tools like pro_watchlist_create and pro_watchlist_delete, which handle 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 clearly states what the tool does but provides no explicit guidance on when to use it versus alternatives. Sibling tool names imply a set of watchlist operations, but the description itself does not mention exclusions 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 only indicate readOnlyHint=true and openWorldHint=false. The description adds substantial behavioral context beyond that: result limits (10 without key, 25 with key), requirement of a Bearer key for premium features, visibility of deal-score, and the mandatory citation of source_url. This is precisely the kind of operational detail that helps an agent understand side effects and requirements.

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 in English, followed by concise, high-value details in French: marketplaces, tier-based result caps, deal-score visibility, and source_url attribution. Every sentence adds a distinct piece of information with no filler. The mix of languages is functional and does not hurt clarity.

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 14 parameters, no output schema, and simple annotations, the description covers the main operational context (aggregation, limits, auth, source_url). It lacks an explicit statement of what fields are returned in each listing, but the parameters mirror likely result attributes, and the mention of source_url provides a key return-value detail. This is adequate for a search tool but not exhaustive.

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 already has detailed descriptions for most of the 14 parameters (coverage 93%), including examples and enum meanings. The description does not add per-parameter semantics; it only uses the generic phrase 'Recherche multi-critères' which is not parameter-specific. Since schema coverage is high, the baseline of 3 is appropriate, and the description does not need to compensate further.

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 opens with a clear verb+resource pair: 'Search used cars for sale in France across all major marketplaces.' It explicitly names the resource (used cars in France), the aggregation scope (major marketplaces), and distinguishes itself from siblings like car_market_stats or estimate_car_price which serve different purposes. The subtitle 'Rechercher des voitures d'occasion' reinforces the action and subject.

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: this is the general-purpose search tool for French used-car listings across aggregated marketplaces. It explains the free tier versus Premium/Pro key behavior, which helps the agent know when this tool is appropriate. However, it does not explicitly name alternatives or exclusion criteria (e.g., 'for pro-only search use pro_search'), so it stops short of full alternative-based guidance.

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