cars-data.com — Car Specs API

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

No annotations are provided, so the description must disclose behavioral traits. It states the function (retrieval of localized specs) which implies read-only, but does not mention error handling, prerequisites, or side effects. The description is adequate but lacks depth for full transparency.

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 a single, well-formed sentence of 7 words. It is front-loaded with the core action and resource, and every word adds value. No redundant information.

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 no output schema and no annotations, the description should cover input validation, output shape, and error scenarios. It only specifies the core function, leaving gaps about how locale is used, what 'specs' entails, and behavior on invalid input. The tool is simple but the description is incomplete.

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 0%, so the description must compensate. It only hints at locale via 'localized specs' and variant count via '2-4 vehicle variants', but does not explicitly explain each parameter's purpose, constraints, or format. This leaves significant ambiguity for an AI agent.

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 compares vehicle variants side-by-side with localized specs. It specifies the resource (variants) and action (compare), and the number range (2-4) further distinguishes it from sibling tools like filter_cars or list_generations.

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 when to use it: when needing a side-by-side comparison of 2-4 variants. However, it does not explicitly state when not to use it or suggest alternative tools (e.g., using get_specs for a single variant). This is a minor gap.

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?

No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as read-only nature, pagination via 'limit' parameter, locale handling, or what the output contains. The description only lists filterable criteria.

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?

A single sentence that is concise and front-loaded with 'Structured catalog filter'. No unnecessary words, every part contributes to the purpose.

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 complexity (10 parameters, no output schema, no annotations), the description is too brief. It lacks details on return values, filtering logic (AND/OR), pagination behavior, and error handling. The sibling 'search_cars' likely requires differentiation.

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 only 30%. The description adds meaning by grouping parameters into categories (e.g., 'power/price range', 'model year', 'EV-only'), which helps for some params. However, it does not clarify all 10 parameters individually, and some like 'locale' and 'limit' are omitted.

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 it's a catalog filter and lists the filterable attributes (fuel type, body type, etc.), indicating the tool's purpose. However, it does not differentiate from the sibling 'search_cars', which likely has similar functionality.

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 like 'search_cars' or 'compare_variants'. The description implies it's for structured filtering but provides no context on when it's appropriate or not.

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?

No annotations are provided, so the description carries full burden. It mentions a behavioral trait (no attribution burden) and CDN source, but lacks details on pagination, URL format, or rate limits. Adequate but not comprehensive.

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?

Single sentence, front-loaded with key information. No unnecessary words.

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 simple input (one parameter) and no output schema, the description is moderately complete. Missing details like number of images, URL format, and error cases, but adequate for a basic image retrieval 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 0%, so description must add meaning. It implies 'variant_id' identifies a vehicle variant, but does not explain what a variant is or how to obtain the ID. Minimal value added beyond 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 returns image URLs for a vehicle variant, with a specific benefit (own CDN, no attribution). It distinguishes from siblings like get_specs or search_cars by focusing on images.

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 on when to use this tool versus alternatives like compare_variants or search_cars. The description does not provide context for usage or 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?

With no annotations, the description adds useful behavioral context: each spec has a confidence score and the response includes last_synced_at. It does not cover side effects, idempotency, or other typical behavioral traits, but the disclosed information is relevant.

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?

Concise and well-structured: front-loaded with core purpose, followed by categorized examples. The listing of spec types is informative without being overly verbose.

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 no output schema, the description mentions confidence scores and last_synced_at but does not detail the structure of spec objects or how categories map to properties. For 180 spec types, more structure 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 description adds little beyond the input schema: it mentions '19 languages' but the schema already has an enum for locale. For variant_id, the description does not repeat the schema's note about coming from search_cars/filter_cars. With 50% schema coverage, the description should compensate more.

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 'Full localized specs for one vehicle variant' and enumerates spec categories (engine, safety, etc.), making the tool's purpose specific and informative. However, it does not explicitly differentiate from sibling tools like compare_variants or search_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?

Provides explicit guidance to surface confidence scores and last_synced_at, which helps agents use the data appropriately. However, no guidance on when to use this tool versus alternatives, or prerequisites (e.g., variant_id must come from search_cars or filter_cars, which is in schema but not description).

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?

With no annotations available, the description carries the full burden of behavioral disclosure. It does not state whether the operation is read-only, requires authentication, or what the response format looks like. The minimal description only hints at output (production years) but lacks critical behavioral details.

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 a single, concise phrase with no wasted words. However, it is too brief and lacks structure, failing to provide necessary details about parameters or output. It is under-specified rather than optimally concise.

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 and simple parameters, the description is incomplete. It does not describe the return format (e.g., list of objects with fields), error conditions, or ordering. The agent has no way to know what to expect after 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 description coverage is 0%, and the description does not clarify the purpose or expected values of the model_id or locale parameters. The locale parameter has an enum but is not mentioned, and model_id is not explained beyond the vague reference 'of a model'. The agent gets no help understanding how to fill these parameters.

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 indicates that the tool returns generations/facelifts of a model with production years, which distinguishes it from sibling tools like filter_cars or get_specs. However, it lacks an explicit action verb like 'list' to match the tool name.

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 provided on when to use this tool versus alternatives such as search_cars or compare_variants. There is no mention of prerequisites or context, leaving the agent to infer usage solely from the name.

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?

With no annotations, the description carries the full burden. It provides concrete details: the size of the search space (102,191 variants, 116 brands, 19 languages), the return type (candidate variant_ids), and extra fields (generation_id, year_from/year_to). It does not mention pagination or rate limits but is otherwise informative.

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 long, front-loads the core functionality, and includes essential details without any repetition or fluff. Every sentence adds value.

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 (large search space, 3 parameters, no output schema), the description covers its purpose, output structure, and usage tie-in to sibling tools. It lacks information on pagination, error behavior, or ordering, but this is acceptable for a straightforward search 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 description coverage is low (33%). The description only adds examples for the query parameter ('bmw 3 series', 'tesla model s'). The limit and locale parameters are not elaborated beyond what the schema provides (integer range and enum list). While the query example is helpful, the description does not compensate fully for the lack of schema descriptions on the other parameters.

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 it is a free-text search across a large corpus, returns candidate variant_ids for use with other tools, and mentions specific attributes (generation_id, year) that disambiguate results. It distinguishes itself from sibling tools like get_specs, get_images, and compare_variants by indicating its output feeds into 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?

The description explains when to use this tool (free-text search for vehicle variants) and what to do with the results (pass to get_specs/get_images/compare_variants). However, it does not explicitly state when not to use it or compare it with alternatives like filter_cars or list_generations.

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