value-us

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

With no annotations provided, the description fully discloses the tool's behavior: returns a numeric score and verification date, and explains the score's meaning (data-confidence, not trustworthiness). This is clear and sufficient for a simple read operation.

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 purpose. Every phrase earns its place, with no redundant or unnecessary 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?

For a simple tool with one parameter and no output schema, the description adequately covers what is returned (score and verification date) and the meaning of the score. It is complete for its complexity level.

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% with a single parameter 'retailer' described as 'Store name or domain.' The description does not add any additional meaning beyond what the schema already provides, so baseline score of 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 the tool retrieves a proprietary VUS score (0-5) and last verification date. It explicitly distinguishes what the score represents (data-confidence signal) and what it is NOT (trustworthiness verdict), making the purpose unambiguous and differentiating from siblings like find_deals and lookup_retailer.

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 for checking data confidence but does not provide explicit when-to-use or when-not-to-use guidance. No direct comparison or exclusion criteria for siblings are given, leaving some ambiguity about appropriate contexts.

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 alone must disclose behavioral traits. It mentions results are 'ranked' and deals are 'verified', but does not explain the ranking criteria, verification process, or how results are ordered. It also lacks detail on pagination behavior beyond the limit parameter. While not misleading, it leaves key behaviors unspecified, earning a middle score.

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 extremely concise—two sentences with no wasted words. The first sentence defines the purpose, and the second provides usage examples. It is front-loaded and 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?

Given that no output schema is provided, the description should explain the return structure. It mentions 'ranked retailers' but does not specify what fields are returned (name, URL, deal details, etc.). Additionally, without annotations, the description lacks information on authentication, rate limits, or whether the tool is read-only. For a tool with three parameters and moderate complexity, this incompleteness is a significant gap.

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 each parameter already has a description. The tool description adds value by providing concrete examples of intent values (e.g., 'free shipping', 'student discount') and clarifying the purpose of country ('so results are relevant to where they can buy'). This enhances understanding beyond the schema, justifying a 4.

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 finds online retailers with specific deals or policies and provides example queries. It uses a specific verb ('Find') and resource ('online retailers'). While it implicitly differentiates from sibling tools (check_retailer_trust, lookup_retailer) by its focus on deals/policies, it does not explicitly mention the distinction, so a 4 is appropriate.

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 example questions that imply when to use the tool, such as 'who has free shipping' or 'which stores give a student discount'. However, it does not include explicit guidance on when not to use it or how it compares to sibling tools, such as specifying that check_retailer_trust is for trustworthiness or lookup_retailer for general info. This leaves usage somewhat implied rather than explicitly guided.

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 describes the tool as looking up data (presumably read-only), but does not disclose if it has side effects, rate limits, or authentication needs. The behavioral disclosure is adequate but incomplete.

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-structured sentence that front-loads the primary purpose. Every part is essential, and there is no fluff. The list of returned items is integrated smoothly.

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 low complexity (1 parameter, no output schema), the description adequately covers the purpose and expected return items. It could be improved by hinting at the return structure, but it is sufficiently complete for an AI agent to understand what the tool does.

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 parameter 'retailer' has 100% schema coverage. The description adds value by providing an example ('Brooklinen' or 'brooklinen.com'), clarifying the format beyond the schema description. However, it does not specify case sensitivity or partial match behavior.

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 as looking up verified deals and store policies for a specific retailer, listing specific items like signup offers, shipping threshold, etc. It differentiates from siblings 'check_retailer_trust' and 'find_deals' by focusing on comprehensive retailer details.

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 explicitly says to input a store name or domain, providing clear usage context. However, it does not explicitly contrast with sibling tools or state when not to use it, but the specificity of the output makes the use case clear.

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