recallsapi.com US recalls

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

Annotations already declare readOnly/idempotent, so the bar is lower. The description adds real value beyond them: it discloses the confidence taxonomy (exact/strong/possible), that early-warning signals come from a distinct NHTSA/CPSC/PRESS/SEC pipeline, and that the receipt option stores a dated record for 400 days fetchable at verify_url and is explicitly not a safety certification.

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 core question and the input flexibility, then covers returns and the signal caveat. The long enumerated sentence is dense but every clause carries information; the return-value detail is somewhat verbose given it appears before the caller has decided to use the tool.

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 must explain returns, and it does: match confidence levels, per-match reasons, agency source links, plus early-warning signals and the receipt artifact. An agent has enough to call it and interpret the result without further documentation.

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 every parameter is already documented in the schema, putting the baseline at 3. The description restates the accepted identifier families (name+brand, UPC/EAN/GTIN, model, NDC, lot) but adds no format or disambiguation rules beyond 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?

Opens with a direct question ('Is this product recalled?') and names the resource and the recall sources checked (CPSC, FDA, NHTSA). It is clearly a product-recall lookup, distinct from the sibling check_vehicle.

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?

'Give whatever you have' tells the agent that any subset of identifiers works, which is useful input guidance, and 'A signal is not a recall' sets expectations. However, it never states when to use this tool versus get_recall or search_recalls, nor any exclusion conditions.

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 cover the safety profile (readOnly, idempotent, openWorld), yet the description adds substantial domain behavior: complaint counts are unverified by NHTSA and not a safety determination, campaigns are filed at model level rather than per-vehicle, and whether a specific vehicle is covered or repaired lives in the maker's records and must be checked by a dealer with the VIN. This is real disclosure beyond 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?

Three sentences, front-loaded with the core capability before the caveats, and every sentence carries information. The closing sentence about dealer verification is somewhat peripheral but still useful for interpreting the result, so it 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?

There is no output schema, so the description should describe the return shape more fully; it only says campaigns and complaint counts are returned. The bigger gap is invocation correctness: with 0 required parameters, the description never clarifies the required VIN-or-YMM combination, leaving an agent able to call it with an incomplete argument set.

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 25% and there are 4 parameters with 0 required, so the description needs to compensate. It explains that the VIN is 17 characters and decoded with NHTSA vPIC and that make/model/year drive the campaign and complaint lookups, but it never states that either a VIN or the make/model/year combination must be supplied, nor whether both may be passed together or how they interact.

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: NHTSA recall campaigns for a vehicle, plus consumer complaint counts, retrieved by VIN or by make/model/year. It is not a tautology and is far more specific than the title. It does not, however, distinguish itself from siblings like get_recall or search_recalls, so an agent cannot confidently route between them from this text alone.

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: two input modes (VIN decoded via vPIC, or make/model/year) are described, which tells the agent what to supply. There is no explicit when-to-use vs. when-not, and none of the sibling tools (check_product, get_recall, search_recalls) are named as alternatives to prefer in a given situation.

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, and openWorldHint=false, so the safety profile is covered. The description adds return content (every identifier plus the agency source link), which is useful, but says nothing about invalid/missing id behavior. With annotations doing the heavy lifting, this is adequate but not rich.

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 front-loaded sentence with no filler; the resource is named before the examples. The four example IDs are slightly listy but each demonstrates a different agency prefix, so they earn their 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 one-parameter, read-only lookup with annotations covering safety and no output schema, the definition is nearly complete: it names the resource, the key format, and roughly what comes back. Only the not-found/error case is unaddressed.

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 sole 'id' parameter has no schema-level description, so the description carries the burden. It compensates with concrete ID examples showing the agency-prefixed naming convention, which is genuinely useful syntax guidance, though it doesn't state the format exhaustively.

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 action and resource: retrieve the full record for a single recall id. The four example IDs (cpsc-, fda-, nhtsa-, fsis- prefixes) make clear this is a by-recall-id lookup, which cleanly separates it from search_recalls and from the product/vehicle checkers.

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 only implied: an agent infers it should call this when it already holds a recall id rather than a product or vehicle identifier. No alternative is named and no condition for choosing this over search_recalls is spelled out.

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 cover the safety profile (readOnlyHint, idempotentHint, openWorldHint=false), so the bar is lower. The description adds useful context on source coverage and result ranking ('newest and most relevant first'), but says nothing about pagination, result volume caps, or cost/latency behavior.

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 tight sentences: the core operation and sources come first, ordering and filtering follow. No filler, nothing redundant with structured fields.

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?

No output schema exists, so the description could usefully say what a result looks like (recall fields, links) and how deep result sets go, but it does not. For a 4-parameter search over 19 categories it is adequate but leaves the agent guessing on result shape and the limit parameter.

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 50%: query and since have descriptions, while limit and category do not. The description hints at category and date filtering, which partially compensates, but the 'limit' parameter and the semantics of the 19-value category enum receive no explanation. Baseline 3 fits.

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: full-text search across US recalls, with the three source agencies named (CPSC, FDA, NHTSA) and the result ordering disclosed. It does not explicitly differentiate itself from siblings like get_recall or check_product, which prevents 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?

Usage is implied by 'filter by category and date' and the word 'search', but there is no explicit statement of when to use this versus get_recall (lookup a known recall) or check_product/check_vehicle (entity-specific checks). The agent must infer the routing itself.

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