value-us
Server Details
Verified deals, store policies & a trust score for thousands of online retailers. No auth.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.8/5 across 3 of 3 tools scored.
Tools have distinct primary purposes: check_retailer_trust focuses solely on trust score, find_deals searches across retailers for specific deals, and lookup_retailer provides comprehensive details for a known retailer. However, lookup_retailer also includes trust score and deals, creating slight overlap. Descriptions help clarify but ambiguity remains.
All tool names follow a consistent verb_noun pattern in snake_case: check_retailer_trust, find_deals, lookup_retailer. The verbs are clear and the structure is uniform.
With 3 tools, the server is slightly below the ideal range but still reasonable for its narrow focus on retailer trust and deals. Each tool serves a distinct core function, so the count feels appropriate for the scope.
The tool set covers the main user needs: checking trust, finding deals by criteria, and looking up detailed store info. There is minor redundancy (trust score appears in two tools) but no critical missing operations for the stated purpose.
Available Tools
3 toolscheck_retailer_trustAInspect
Get value.us's proprietary VUS trust score (0-5) for a retailer and when its data was last verified - useful for 'is this store legit / trustworthy / safe to buy from'.
| Name | Required | Description | Default |
|---|---|---|---|
| retailer | Yes | Store name or domain. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must cover behavior. It discloses the return type (score and verification date) but omits details like what happens if a retailer is not found, rate limits, or data freshness. Adequate but could be more thorough.
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 sentence that front-loads the main output and purpose with no filler or unnecessary details.
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 required parameter and no output schema, the description sufficiently covers purpose, output, and usage context. However, it could mention error handling or data source limitations.
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% and the single parameter 'retailer' is clearly described as 'Store name or domain.' The description adds no extra meaning beyond the schema, meeting the baseline with no added value.
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 explicitly states the tool returns a proprietary VUS trust score (0–5) and last verified date, with clear use cases like assessing if a store is legit or safe. It distinguishes from sibling tools (find_deals, lookup_retailer) by focusing on trustworthiness.
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 suggests when to use (e.g., 'is this store legit'). While it doesn't explicitly state when not to use or name alternatives, the context is clear enough for a simple query tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_dealsAInspect
Find online retailers that currently have a specific kind of verified deal or policy, ranked. Use for questions like 'who has free shipping', 'which stores give a student discount', or 'what's on sale now'.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10). | |
| intent | Yes | The deal/policy type to find retailers for. | |
| country | No | 2-letter market the shopper is in (e.g. 'US', 'GB', 'DE'), so results are relevant to where they can buy. Defaults to 'US'. Pass 'all' for every market. Retailers with no declared market are always included. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It mentions results are 'ranked' but does not explain ranking criteria or disclose any side effects, rate limits, or authentication needs.
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, no fluff. First sentence states core purpose, second gives examples. Front-loaded and efficient.
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?
Despite no output schema, the description covers purpose, usage, and parameter context adequately. Some missing details (e.g., output format) but acceptable for a search tool.
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 clear descriptions. The description adds value by providing usage examples and context (e.g., 'ranked'), going 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?
The description clearly states the tool finds retailers with specific deals/policies and provides example queries. It distinguishes from siblings (check_retailer_trust, lookup_retailer) by focusing on deal discovery.
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?
Explicit usage examples are given, but no exclusions or contrasts with alternatives are provided. The examples cover the common intents well.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lookup_retailerBInspect
Look up verified deals, store policies and trust info for a specific online retailer: signup/newsletter offer, free-shipping threshold, return window, price-match, gift cards, student/military discounts, current sale, and the value.us trust score. Input a store name or domain.
| Name | Required | Description | Default |
|---|---|---|---|
| retailer | Yes | Store name or domain, e.g. 'Brooklinen' or 'brooklinen.com'. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It implies a read-only lookup operation by listing retrieved data, but it does not explicitly state it is non-destructive, discuss authentication needs, rate limits, or error handling. The description is adequate but not thorough.
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 efficiently conveys the tool's purpose and expected input. Every word adds value, and there is no extraneous content.
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 (one parameter, no output schema, no annotations), the description is reasonably complete in outlining the returned data. However, it omits details about behavior on missing retailers, pagination (if any), and how the tool differs from sibling tools, leaving some gaps for an AI agent.
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 input schema already describes the single parameter ('retailer') with an example, achieving 100% schema coverage. The description adds only a generic restatement ('Input a store name or domain'), providing negligible additional semantic value beyond what the schema offers.
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 it looks up verified deals, store policies, and trust info for a specific retailer. It enumerates specific data points (signup offers, free shipping, etc.), making the purpose concrete. However, it does not explicitly differentiate from sibling tools like check_retailer_trust or find_deals, which may have overlapping functionality.
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 only instructs to 'Input a store name or domain,' which is parameter guidance, not usage guidance. It does not specify when to use this tool versus alternatives (e.g., check_retailer_trust for trust-focused queries or find_deals for broader deal searches), nor does it mention prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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