shoporacle

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the output ('ranked list with prices, ratings, and reviews') and external sources, but does not disclose whether the operation is read-only, requires authentication, has rate limits, or how it handles invalid categories or network errors. This leaves the agent without important behavioral context.

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 action and scope. It avoids redundant details and includes only relevant claims about the return format and use case. Every sentence earns its place, and there is no 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?

The description conveys the primary purpose and output, but for a tool with 4 parameters, no annotations, and no output schema, it lacks some completeness. It does not clarify that 'category' is effectively required (despite schema saying 0 required), nor does it mention default behaviors or edge cases like empty results. The description is adequate but not exhaustive.

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%, so the schema already documents all four parameters with clear descriptions. The tool description adds no additional parameter semantics beyond pointing out 'category' as the filter. Since schema coverage is high, the baseline of 3 applies; the description does not enhance or clarify parameters further.

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: 'Get top-selling products in a category from Amazon or Google Shopping.' It uses a specific verb ('Get'), names the resource ('top-selling products'), and scopes it by source and category. This distinguishes it from sibling tools like product_search or compare_prices, which have different functions.

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 market research ('Great for market research') but provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or exclusions, such as when to prefer product_search or compare_prices. The usage context is hinted but not fully developed.

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 burden of behavioral disclosure. It discloses the return values (best price, worst price, potential savings, ranked list), giving some transparency. However, it omits potential caveats such as data freshness, possible missing products across sources, or that this is a read-only operation. The description adds moderate value beyond the schema but does not fully disclose behavioral limitations.

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 main action ('Compare prices...') and then lists the return values. Every word contributes to understanding the tool, with no repetition or wasted language.

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?

The description effectively communicates the core purpose and return values, which is sufficient for a 3-parameter read-only tool with no output schema. It does not explain the exact structure of the ranked price list, but the high-level return enumeration covers the essential context. Given the tool's simplicity and schema coverage, the description is mostly complete.

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%, so the schema already fully documents all three parameters (limit, query, country) with their meanings and defaults. The description does not add additional parameter semantics beyond what the schema provides, so the baseline score 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 clearly states the tool's purpose with a specific verb ('Compare') and resource ('prices for the same product across Google Shopping, Amazon, and eBay'). It distinguishes itself from siblings like 'competitor_pricing' or 'product_search' by naming the exact comparison sources and the nature of the output.

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 the tool is used for comparing prices across specific marketplaces, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among sibling tools. There is no clear direction on choosing this over similar tools like 'competitor_pricing'.

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 the full burden of behavioral disclosure. It does describe the output ('which is cheaper, price difference, and offers from each source'), but it does not explicitly state whether the operation is read-only, whether authentication is needed, or any limitations such as data freshness or marketplace coverage.

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 concise sentences that directly state the purpose and output. Every word adds value, with no redundant or extraneous 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 comparison tool with 3 parameters and no output schema, the description is largely complete: it names the inputs, the action, and the return information. However, it would be more complete with a brief note distinguishing itself from the sibling 'compare_prices' 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?

The input schema has 100% parameter description coverage, so the schema itself provides full parameter semantics. The description adds minimal value beyond echoing that product_a and product_b are specific products or brands, and it does not clarify country formatting or the apparent mismatch where required parameters are marked as required in their descriptions but not in the required array.

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 'Compare pricing' with a clear resource ('two specific products or brands') and scope ('across all marketplaces'). It is clear and functional, but it does not differentiate from the similarly named sibling tool 'compare_prices,' 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 implies its intended usage: comparing two specific products or brands. However, it provides no explicit guidance on when to use this tool versus alternatives, such as the sibling 'compare_prices,' nor does it state any exclusions.

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 transparency burden. It does disclose that the tool reports on server health and API connectivity, implying a live status check, and mentions additional static configuration data. However, it does not state whether the operation is read-only, whether authentication is required, or if any rate limits apply, leaving some behavioral ambiguity.

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, concise sentence consisting of a comma-separated list. It contains no fluff or redundancy, front-loads the core information (server health and connectivity), and earns its place by clearly conveying the tool's scope in minimal space.

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 (no parameters, no output schema), the description adequately covers the main categories of information it returns. It is not exhaustive—for instance, it doesn't specify the response format—but it is sufficient for an agent to understand the tool's purpose and decide to call it.

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 tool has zero parameters and an empty schema, so there is nothing to explain. Per the rubric, a no-parameter tool is a baseline 4. The description appropriately focuses on what the tool returns instead of parameter details.

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 enumerates the tool's outputs: server health, API connectivity status, supported sources and countries, tool list, and pricing info. Although there is no explicit verb, the resource scope is specific and distinct from the sibling pricing-focused tools, which all deal with product or price data.

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 usage guidance is provided. The description does not indicate when to use this tool versus others, nor does it mention prerequisites, alternatives, or exclusions. It simply lists what information the tool returns, leaving the agent without direction on when to invoke it.

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 discloses output facets (budget/mid/premium/luxury, rating comparison, alternatives) but doesn't mention read-only status, data freshness, or any limitations. Some behavioral context is given, but key aspects remain unspecified.

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, well-structured sentence that front-loads the core purpose and follows with specific examples via em-dash. Every word contributes value; no wasted 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?

Description outlines the main output categories and mentions the auto-derived category (via schema parameter). Though no output schema exists, the description gives enough for a simple query but lacks exhaustive structure or edge cases.

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?

Input schema has 100% coverage for all three parameters (query, country, category), so the schema handles parameter meaning. The description adds no additional parameter details, leaving baseline of 3.

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?

Description uses the specific verb 'Analyze' and a clear resource ('where a product ranks in its category'), then enumerates concrete outputs (price positioning, rating comparison, cheaper/pricier alternatives). This distinguishes it from sibling tools like compare_prices or product_search.

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 conveys the use case (analyzing a product's market positioning) without explicitly naming alternatives or exclusions. The context is evident, but there's no direct comparison to sibling tools like competitor_pricing or bestseller_list.

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 transparency burden. It does disclose the core behavior (notifications on price drops) and action semantics, but omits details like persistence, delivery method, permission requirements, or side effects of set/delete. This is partial transparency, not full.

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 reasonably short but contains redundancy: the first sentence lists the same actions in the third sentence. It is front-loaded with the main purpose, but the action list is repeated, making it slightly less efficient than it could be.

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?

This is a stateful tool (set/delete alerts) with no output schema and no annotations. The description does not explain what the tool returns (e.g., check results, list format, success/failure), nor does it clarify the relationship between asin and query or country behavior. It is incomplete for an agent to invoke confidently.

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 no new parameter meaning beyond the schema; it repeats some action-related info but does not clarify how asin/query interact, default values, or formatting. It neither improves nor harms the schema's clarity.

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 manages price alerts with specific actions (set, check, list, delete) and the alert trigger (product drops below target price). It does not explicitly differentiate from sibling tools like track_price or stock_monitor, so it stops short of full sibling distinction.

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 lists what actions can be performed but gives no guidance on when to choose this tool over alternatives, nor does it mention prerequisites or exclusions. Usage is implied by the action names, but there is no explicit context or when-not-to-use guidance.

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?

Even without annotations, the description discloses key behavioral traits: it is a read-only operation ('View'), it includes trend analysis, and it depends on prior data from track_price. This goes beyond the tool name and schema, providing useful context about the tool's dependency and output nature.

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, front-loaded with the primary purpose, and includes the necessary dependency note. Every sentence adds value without redundancy.

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?

The tool is simple with no output schema, but the description covers the core functionality, trend analysis, and the prerequisite. It could mention what happens if no history exists or how asin/query are used in combination, but overall it provides a solid understanding for a straightforward 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?

The input schema has 100% description coverage for all three parameters (asin, limit, query), each with clear descriptions. The tool description does not add additional parameter semantics beyond the schema, so 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 clearly states the tool's function: 'View stored price history for a product with trend analysis (rising/falling/stable)'. It specifies the resource (price history) and the action (view), and distinguishes itself from siblings like track_price by referencing it as a prerequisite. The mention of min, max, and average prices further clarifies 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: it should be used after track_price has been called to build up history data. This implies the tool is for historical analysis, not for real-time tracking. It does not explicitly exclude other alternatives (e.g., compare_prices), but the prerequisite gives a clear usage condition, qualifying as 'clear context, no exclusions'.

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 transparency burden. It discloses return content (prices, ratings, links) and key behavioral options (country, sort). It does not mention per-source limits or that query is effectively required, but those are partially covered by the schema.

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 with no fluff. It front-loads the core action and target, then provides high-value detail on returns and options.

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 tool with 5 parameters and no output schema, the description plus schema provides a solid overall picture. However, the 'required' ambiguity for query (schema says required in description but not in required array) and lack of return structure prevent a perfect score.

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%, so baseline is 3. The description adds context about country and sorting, but these are already well-described in the schema's parameter descriptions, so no significant extra meaning is provided.

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') with a clear resource ('products across Google Shopping, Amazon, and eBay') and mentions return content ('prices, ratings, links'). This scope distinguishes it from siblings like bestseller_list or price_alert, even without explicitly naming alternatives.

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 through the description's mention of multi-marketplace search, country-specific results, and sorting options, but it does not explicitly state when to use this tool versus alternatives like compare_prices or track_price.

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 discloses that the tool fetches ratings and reviews from Amazon, but doesn't mention authentication, rate limits, or edge cases like empty results or ambiguity between asin and query. Basic behavior is covered, but depth is limited.

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, front-loaded with the primary function. The second sentence adds the usage context. Every word earns its place with no redundancy.

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 retrieval tool with no output schema or annotations, the description covers the main output (ratings, breakdown, top reviews). It could be more explicit about parameter precedence or expected behavior when neither asin nor query is provided, but overall it is sufficient for a straightforward 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 description coverage is 100%, so each parameter is already documented with clear descriptions. The tool description adds no additional parameter context, such as how to choose between asin and query, which would be useful. Baseline 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 review ratings, score breakdown, and top customer reviews for a product from Amazon. This specific verb+resource combination distinguishes it from sibling tools for pricing, search, or stock.

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?

It provides a clear use case: 'Useful for purchase decisions and product quality assessment.' This implies when to use the tool, though it does not explicitly name alternatives or say when not to use it.

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 behavioral disclosure. It mentions what information is shown (in-stock/out-of-stock, prices, delivery info, sellers) but does not clarify read-only nature, data sources beyond platform names, or behavior when no match is found. The description adds some context but leaves gaps in 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?

The description is two sentences with a clear, front-loaded verb and resource. Every word contributes to understanding the tool's purpose and outputs, with no filler or redundancy.

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?

The description provides a general outline of returned data (stock status, prices, etc.) but lacks details on parameter combinations, the necessity of at least one identifier, or edge cases. Without an output schema, it should clarify more about invocation constraints, making it partially complete.

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 documents all three parameters with descriptions (100% coverage), so the baseline is 3. The description does not add additional meaning about parameter usage, such as whether 'asin' and 'query' are mutually exclusive or if at least one is required. No extra value is provided 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 verb 'check' targeting 'product availability and stock status' across Amazon and Google Shopping. This distinguishes it from price-focused siblings like compare_prices and product_search, making the tool's purpose specific and unambiguous.

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 the tool is for checking availability and stock, which suggests when to use it, but it does not explicitly state alternatives or exclusions. No mention of when to prefer compare_prices or product_search is provided, leaving usage guidance implicit rather than explicit.

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 the transparency burden. It discloses that the tool builds price history over time, implying persistence, but it does not clarify whether tracking has side effects, requires authentication, or what the response contains.

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 concise sentences, with the primary purpose front-loaded and no filler. Every phrase adds useful information about either the tool's function or how to provide input.

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 and no annotations, the description should explain return values and side effects, but it only explains input options. It also does not mention the `country` parameter or when to prefer this tool over closely related siblings like `price_history` or `price_alert`.

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 fully covers all three parameters, providing a baseline of 3. The description adds semantic value by framing 'asin' as 'exact product tracking' and 'query' as 'best-price discovery', which clarifies their intended use beyond the schema descriptions.

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 tracks current product prices and builds price history, which is a specific action on a clear resource. It distinguishes between two input modes (ASIN vs. search query), though it does not explicitly differentiate itself from the sibling `price_history` tool.

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?

It provides explicit guidance on when to use an ASIN versus a search query, which is directly useful for invocation. However, it does not mention alternatives like `price_alert`, `price_history`, or `compare_prices`, so there is no comparison with sibling tools.

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