Car Image API catalog

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

Annotations already cover readOnly, idempotent, non-destructive and closed-world, so the description's remaining job is to add context. It does add two useful facts: the call is 'Free' (no credits consumed) and the response enumerates specific reference sections. It says nothing about size limits or truncation beyond the word 'compact,' so the addition is helpful but modest.

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?

One dense sentence front-loads the cost signal ('Free') and the core purpose, with the filter caveat trailing. Tight overall, though the parenthetical inventory is long and the 'with SDK ... and CLI ... equivalents' clause slightly crowds the sentence.

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 correctly compensates by listing what the reference contains, so an agent knows what it will get back. It is nearly complete for a documentation-style tool; only the granularity/format of the returned reference (e.g., verbosity, section structure) is left unspecified.

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 the endpoint parameter's meaning, maxLength and examples are already fully documented in the schema. The description only restates that the filter is optional, adding no syntax or matching semantics (e.g., substring vs keyword matching) beyond the schema; baseline 3 applies.

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 concrete verb+resource ('Return a compact reference of the Car Image REST API') and enumerates the payload contents (methods, paths, parameters, credit costs, headers, error codes, agent notes), which clearly separates it from the operational siblings like preview_car_image or get_pricing.

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?

Gives a clear condition for use: 'for writing code that calls the API directly,' and notes the SDK and CLI equivalents so an agent knows this is the raw-HTTP path. It does not explicitly say when to prefer a sibling such as search_help, and the filter guidance ('Optional endpoint filter') is thin, so it stops short of full 5-level routing.

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 it is a safe idempotent read; the description adds real behavioral context: it is free and needs no account, prices are not metered ('There is no overage'), credits are not sold separately, and checkout_url works pre-signup with no charge until the human pays. That last point is critical agent-facing behavior the annotations cannot express.

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 key fact (free, no account) and ends with the usage directive. It is a single dense paragraph with several long parentheticals defining 'vehicle image' and the checkout flow; those earn their place but the block could be broken up for scanability.

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?

An output schema exists, yet the description still summarizes the returned entities well enough that an agent can decide whether to call it and how to relay results. For a no-argument read tool, nothing needed to invoke it correctly 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?

Zero parameters, so there is no parameter semantics to explain; baseline is 4. The description does not add parameter detail, but none is needed.

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 and resource ('The pricing document') and then enumerates exactly what it contains: monthly/yearly price, vehicle image allowance, credits, tier limits, add-on packs, free tier. An agent knows precisely what this returns and can distinguish it from describe_api or list_image_options.

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?

Gives a clear trigger ('Use before explaining costs or choosing a plan') and an operational instruction ('hand it to the human with the price'). It does not name the sibling tools it supersedes, but none of the listed siblings is a plausible substitute for pricing lookup, so the guidance is effectively unambiguous.

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 declare readOnlyHint, idempotentHint, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds something the annotations do not: 'Free,' which tells the agent there is no cost or quota concern in calling it. It does not address result size or pagination, but the output schema exists to carry return-shape detail.

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, front-loaded with the free/cost signal and then a dense enumeration of catalog contents followed by the usage trigger. The mid-sentence parenthetical about yaw angles, aliases, and hex swatches makes the first sentence long, but every element listed corresponds to something an agent would need to know.

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, side-effect-free catalog tool with an output schema, this is essentially complete: it says what is enumerated, what it costs to call, and when to reach for it. Return-value structure is correctly left to the output schema rather than restated.

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 takes zero parameters, so the baseline is 4 and the schema has nothing to explain. The description still adds a useful semantic rule the schema cannot express: the fifteen preset colors have hex swatches, and any other paint must be supplied as a hex color, which is guidance an agent needs when constructing a downstream preview request.

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 gives a specific verb ('List') and enumerates exactly what is covered: camera views with yaw angles and aliases, fifteen preset colors with hex swatches, size presets, output formats, pricing, and catalog coverage. That is far more specific than the title alone. It stops short of a 5 because it never distinguishes itself from siblings like get_pricing or describe_api, whose domains it partly overlaps.

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?

'Use it when you need to validate or explain what can be requested' gives a clear triggering context rather than leaving usage implied. There are no explicit exclusions or named alternatives, so it lands at 4 rather than 5 — the agent must still infer why it would choose this over get_pricing for the pricing portion.

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 readOnly/idempotent/non-destructive annotations, it discloses cost (free, no account), output format and size (600 px WebP + markdown), that all stored views and paints are returned, and that no rendering or recoloring happens server-side. The 'Never renders' sentence is grammatically tangled ('...is an image from the account server'), which slightly blunts an otherwise strong disclosure.

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-loading 'Free, no account needed' is good, and little is pure filler. However the second sentence is an overstuffed colon construction that is hard to parse on first read, and parts duplicate the schema's parameter descriptions.

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 preview tool with full annotation coverage, a 100%-documented schema and an output schema, everything an agent needs is present: cost, output shape, fallback path with credit cost, id provenance, and how to present the result to the human.

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 the descriptions of vehicle, view and color already carry the semantics, and the prose largely restates them (id format, view preference, no recoloring). It adds the useful routing hint that the id comes from resolve_vehicle/search_vehicles and tells the agent to report the actual view/color when it differs from the request, but that is marginal over the 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 names a specific verb+resource (fetch the preview image already published for a catalog vehicle) and states the exact payload: a 600 px WebP, markdown, the view/paint it is in, and all stored views/paints. It also carves out what it is not ('Never renders') versus create_car_image_urls. The core statement is buried in clause-heavy prose, which keeps it just short of a 5.

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 gives explicit when-to-use (a vehicle with a picture already stored on its public page) and when-not (any other view, paint or size, or a vehicle with nothing stored – go to create_car_image_urls for 1 credit). It also tells the agent where the vehicle id comes from: resolve_vehicle or search_vehicles.

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 declare readOnly, idempotent, openWorld and non-destructive, yet the description adds real value beyond them: cost ('Free'), the returned confidence tiers, alternative candidates, and a specific mismatch warning that params.year may differ from the requested year (extracted.year). The human-in-the-loop instruction for ambiguous resolutions is exactly the behavioral context annotations cannot carry.

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-loads the key qualifier ('Free') and the primary use case, then flows into outputs, downstream wiring, and edge-case handling. Several clauses are dense and run together, but no sentence is filler — the only cost is readability, not waste.

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 one-parameter resolver with an output schema, this covers everything an agent needs: when to call it, what it returns (confidence, candidates), how to consume vehicle_id downstream, and how to handle ambiguity or a year mismatch. Nothing material is left to inference.

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 single query parameter is already 100% documented in the schema, so the schema does the heavy lifting. The description still adds meaning by specifying which fields the free text should contain (year, make, model, view, color when named) and clarifying that make/model are parsed with the same rules the image tools use, which tells the agent why the phrasing matters.

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 concrete verb and transformation: turn a free-text request into the catalog vehicle plus exact get_car_image parameters, and enumerates those parameters (vehicle_id, make, model, year, view, color). The worked example ('red 2018 Mazda MX-5 Miata side view') makes it unmistakable against siblings like search_vehicles or list_image_options.

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?

Explicitly positions itself as 'the first call for any picture of a car, including one you chose yourself,' which is clear when-to-use routing, and names the downstream consumers (get_car_image, create_car_image_urls, create_3d_model). It also gives an escalation rule for low confidence or multiple candidates, but never states when to prefer search_vehicles or list_image_options instead, so the alternative-space is only partially covered.

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 adds substantial context beyond the annotations: it declares the tool free, discloses that queries are recorded privately to improve articles (which explains the readOnlyHint=false side effect of logging), and sets output-handling rules about citing sources and not inventing answers. It does not mention authentication or rate limits, so it stops short of full behavioral disclosure.

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?

Three sentences, front-loaded with the cost and capability, then usage constraints, then output rules. The middle topic list ('pricing, terms, licensing, manufacturer rights, policies, Enterprise, delivery and account support') is a dense enumeration that could be trimmed, but the rest 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?

An output schema exists, so return values need no explanation, and the annotations plus the logging disclosure cover the behavioral profile. The one material omission is the report_gap flag for flagging unanswered questions, which is central to the described escalation workflow but only implied by 'escalate unanswered parts to support'.

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 100%, so both parameters are already documented in structured data. The description reinforces the query constraints ('one question at a time', no personal information or full emails) but never mentions the report_gap parameter, so it adds little new meaning over the 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 states a specific verb and resource (find source-linked answers) and enumerates the covered topic domains: pricing, terms, licensing, manufacturer rights, policies, Enterprise, delivery and account support. It is clear what the tool does, but it does not differentiate itself from the sibling get_pricing, which overlaps directly with the 'pricing' topic it claims.

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 context is implied rather than spelled out: 'Suitable for drafting replies to customer questions' and 'Submit one question at a time' guide the agent, and escalation of unanswered parts is named. However, no when-not-to-use rule or routing to the obvious alternative (get_pricing) is given, leaving the agent to infer which tool wins for pricing questions.

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 declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds value beyond that: it says the call is free, that results are canonicalized names, that a model hit carries a stable per-year vehicle id, and that the id should be passed as `vehicle` to the image and 3D tools — useful chaining context. It stops short of stating result caps or truncation behavior beyond the schema's limit.

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?

Three sentences, front-loaded with the leading qualifier "Free" and the scope, and every sentence carries distinct information (capability, return shape, routing). The parenthetical about ids is dense and slightly hard to parse, which keeps it off a 5.

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 3-parameter read tool with an output schema and full annotation coverage, this description supplies everything an agent needs: scope, matching rules, return shape, id handoff to sibling tools, and routing versus resolve_vehicle.

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; the description adds meaning by explaining the query semantics ("fewer words find more: every word must be in the name of one catalog vehicle") and typo tolerance, and by linking the year dimension to per-year id granularity. It does not elaborate on `limit` or on how `year` interacts with ranking, so it is not exhaustive.

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 and resource (browse the vehicle catalog) plus concrete scope (1,608 makes, 42,321 models, 1990-2027) and matching behavior (make/model with typo tolerance). It also names what it is not — resolve_vehicle — so an agent can distinguish the two without opening either schema.

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?

Explicitly lists the intended cases (list a make's models, check which years exist, let a human choose between similar models) and the exclusion with a named alternative: when the car is already known, resolve_vehicle is the one call. Both when-to-use and when-not-to-use are covered.

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