value-us

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

No annotations are present, so the description carries the full burden. It adds valuable context by explaining what the VUS score measures (data confidence) and explicitly warns it is NOT a verdict on store trustworthiness. However, it does not disclose potential error behavior, read-only guarantees, or rate limits, which would be additional useful behavioral traits.

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-loading the core function and adding a crucial caveat. Every word earns its place with no redundancy, making it highly concise and well-structured.

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 covers the return values (score and last verified time) and the essential interpretation caveat. It lacks usage guidance and edge-case behavior, but these are partially covered by other dimensions, so the completeness is solid for the tool's complexity.

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 only parameter 'retailer' already has a clear schema description ('Store name or domain.'), giving 100% schema coverage. The description adds no extra parameter semantics, so the baseline 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 value.us's VUS score (0-5) and last verified time for a retailer, using a specific verb ('Get') and resource. It also clarifies that the VUS score is not a trust verdict, which distinguishes its purpose from what the name implies, but it does not explicitly reference sibling tools to fully differentiate 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?

No guidance is provided on when to use this tool versus alternatives like find_deals or lookup_retailer. The description implies it is for data-confidence checks but does not state conditions, exclusions, or recommend sibling tools for trust-related 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?

With no annotations, the description carries the full burden. It adds behavioral context by saying 'verified deal or policy' and 'ranked,' implying results are filtered/curated and ordered. Yet it does not disclose potential limitations (e.g., data recency, market availability behavior already in schema, whether results include retailer contact info) or any side effects. For a read-only search tool this is acceptable 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?

Two sentences total. The first sentence is front-loaded with the primary action and result, the second gives clear, relevant usage examples. No filler, no repetition of schema fields, 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 search tool with 3 params and no output schema, the description is sufficiently complete. It states the action, the result type (ranked retailers), and common use cases. It could explicitly mention that the output is a ranked list, but 'ranked' already implies that. The sibling tool names provide additional context for disambiguation. This is above the minimum viable for the tool's complexity.

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 baseline is 3. The description adds value by mapping example questions to specific intents (e.g., 'free shipping' corresponds to the free-shipping enum), which helps the agent pick the right intent. It also hints at the 'country' parameter implicitly with 'where they can buy' in the schema, though the description itself doesn't mention country beyond examples. Overall, it enriches the schema's parameter docs enough to justify 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 opens with a specific verb+resource: 'Find online retailers that currently have a specific kind of verified deal or policy, ranked.' This clearly distinguishes the tool from siblings like check_retailer_trust (which likely evaluates trust of a given retailer) and lookup_retailer (which retrieves retailer details). It also provides concrete example queries ('who has free shipping', 'which stores give a student discount'), making the intent unmistakable.

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 guidance is explicit via 'Use for questions like...' followed by three representative queries that map to intent enum values. This tells the agent when to apply the tool. However, it does not explicitly mention when not to use it or name alternatives like check_retailer_trust or lookup_retailer, so it stops short of a full comparison.

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 burden of explaining behavior. It does disclose the data returned (signup offers, shipping thresholds, return windows, etc.) and the VUS score, but it does not mention potential limitations such as missing data for obscure stores, freshness, or what happens when a retailer isn't found. 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?

The description is a single, information-dense sentence that front-loads the core purpose ('Look up verified deals and store policies for a specific online retailer') followed by a clear, comma-separated list of included data. Every word earns its place; no filler or repetition.

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 simplicity (single parameter, no output schema), the description is comprehensive: it lists the return data fields and the input requirement. It does not describe return format or error handling, but the absence of an output schema is mitigated by the explicit enumeration of what the tool returns. A score of 4 reflects its completeness for typical use.

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% and the parameter description already includes examples ('Brooklinen' or 'brooklinen.com'). The tool description reiterates 'Input a store name or domain' but adds no new semantic information beyond what the schema provides. The baseline of 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?

The description uses a specific verb ('Look up') and resource ('verified deals and store policies for a specific online retailer'), clearly distinguishing this from sibling tools like find_deals (general deal search) and check_retailer_trust (trust rating only). It enumerates the exact data categories returned, leaving no ambiguity about the tool's scope.

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 clear context: use this tool when you need verified deals and store policies for a specific named retailer. It implies a targeted lookup ('Input a store name or domain') and contrasts with siblings by focusing on a single retailer. However, it does not explicitly state exclusions or when to prefer alternatives, so it falls short of a 5.

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