Verity

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

With no annotations provided, the description carries the full burden of disclosing behavior. It does state what the tool returns: facts with official citation URL and last-verified date. It does not disclose potential side effects (though 'Get' implies read-only), behavior when the optional market is omitted, error conditions, pagination, or result limits. For a simple read tool this is adequate but not rich; it leaves the agent to assume a safe read operation and a certain response shape.

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-crafted sentence followed by a brief example. It front-loads the core function, then clarifies with an illustrative invocation and describes the output. Every word earns its place; there is no repetition or 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 two-parameter tool with no output schema and no annotations, the description gives the essential purpose and return values, but it leaves gaps: the meaning of a null market is undefined, the exact shape of returned facts is only hinted at, and there is no guidance on invalid inputs or unsupported subjects/markets. These gaps are not critical for a simple getter, but they prevent the description from being fully self-sufficient.

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 for both parameters. It does define subject as a product subject and market as a target market, with example values ('childrens_products', 'US'). However, it does not explain that market is optional with a default of null, does not constrain possible subject values, and does not specify expected formatting. The examples add some meaning beyond the raw schema, but the optionality and semantics of a null market are left ambiguous.

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 ('Get') and a clear resource ('cited compliance requirements for a product subject in a given market'), and it adds the concrete example subject='childrens_products', market='US'. This makes the primary purpose immediately understandable. It does not explicitly differentiate this tool from its siblings (list_changes, search_recalls, verify), but the distinct action and resource make confusion unlikely, so it falls 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?

The description gives an example invocation and implies it should be used when an agent needs cited compliance data for a product subject and market. However, it provides no explicit guidance on when to prefer this tool over siblings like search_recalls or verify, and there are no statements about when not to use it. The usage context is implied through the example, not explicitly scoped.

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?

There are no annotations, so the description carries the full burden. It does disclose useful response content (recalls, rule changes, each with a source URL), but it omits behavioral details such as limit/pagination behavior, ordering, whether the timestamp cutoff is inclusive, and what an empty result looks like.

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 focused sentence front-loads the core action and required timestamp, then clarifies the output contents. There is no filler, no redundant restatement of the tool name, and every phrase 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 simple list call, the core is covered: required timestamp, optional limit, and returned entities with source URLs. However, there is no output schema and no description of the return envelope, pagination, or limit semantics, leaving an agent to guess when handling large result sets.

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 does add real value for `since` by specifying ISO-8601 and providing an example. However, `limit` is left entirely to its schema metadata (integer, default 100), with no explanation of what it caps or how pagination works, so compensation is incomplete.

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 specific verb ('List') and a distinct resource ('ground-truth changes since an ISO-8601 timestamp'), then adds what is included (newly published recalls, rule changes, source URLs). This clearly separates it from sibling tools like search_recalls and get_requirement, which are about finding or fetching individual records rather than consuming a time-based changelog.

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 establishes when to use the tool: to list ground-truth changes after a given timestamp. It does not explicitly state when not to use it or name sibling alternatives, so it falls just short of full guidance, but the intended use case is 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?

With no annotations provided, the description carries the full behavioral burden and does so well: it discloses that only 'cited recall records from the store' are returned, that each result includes a source URL and match score, and that no-match cases return an empty list instead of fabricated guesses. It omits edge-case details like pagination or auth, but the core behavior is clearly communicated.

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 three sentences with no filler: the first defines scope, the second describes return shape, and the third defines empty-result behavior. It is front-loaded and every sentence contributes 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?

For a simple search tool with no output schema and no annotations, the description is quite complete: it states what is searched, what results contain, and what happens on no match. The only minor gaps are the semantics of market and limit, but those are optional parameters with defaults.

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 adds meaningful semantics to the required query parameter by defining acceptable inputs as product name, brand, model, or UPC. However, it does not explain the limit or market parameters beyond what their names and defaults imply.

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 ('Search') and a specific resource ('official CPSC recall records'), and enumerates supported search keys: product name, brand, model, or UPC. This clearly differentiates the tool from sibling tools like get_requirement, list_changes, and verify, which address different resources and actions.

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 context makes it clear this tool is for searching recall records, but there is no explicit guidance about when to use it versus alternative tools, nor any stated exclusions. Usage context is implied rather than directly 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?

With no annotations, the description carries the full burden and does so thoroughly: it discloses citation-only returns, explicit 'no verified record' reporting, never synthesizing an answer, and the meaning of absence. This is substantial behavioral context that goes well beyond the bare tool name.

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, purpose front-loaded, and every clause adds meaningful behavioral or usage information. There is no filler, repetition, or unnecessary background.

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 tool with no output schema, the description covers input semantics, return type, no-match behavior, and interpretation of absence. An agent has enough information to invoke the tool correctly and interpret its results.

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 schema has a single query parameter with 0% description coverage. The tool description clarifies that query should contain a factual claim or product query, adding meaning beyond the schema's bare 'Query' title. It lacks examples or formatting details, but the core semantic is clear.

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: checking a factual claim or product query against the store and returning cited recall/fact records. It also distinguishes itself from search_recalls by emphasizing return-only-cited-records and the no-synthesis/absence behavior, even though sibling tools are not named.

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 first sentence provides a clear use case: use this tool for factual claims or product queries. The absence semantics also guide interpretation of no-match results. However, it does not explicitly mention when not to use it or which sibling tool to use instead, so it stops short of full alternative routing.

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