ecommerceoracle

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns full product details and prices from multiple merchants, which is the primary behavioral output. It doesn't mention failure modes or network dependencies, but for a simple lookup tool this is adequate.

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, front-loaded with the verb 'Look up' and the resource 'product'. Every word adds value, with no fluff 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?

For a simple lookup tool with two optional-looking parameters and no output schema, the description is sufficient. It states the high-level return payload. The only minor gap is not clarifying which parameter to prefer, but the schema describes 'ean' as an alternative name, partially covering this.

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%, with both parameters (ean and barcode) documented. The tool description adds no extra parameter semantics beyond what the schema already provides, so a 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 does a product lookup by EAN/UPC barcode, a specific verb+resource combination. It distinguishes itself from sibling tools like product_search and price_comparison by focusing on barcode input, making the purpose 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 gives clear context: use this tool when you have an EAN/UPC barcode. It doesn't explicitly list alternatives or exclusion criteria, but the barcode-specific wording makes the intended use case obvious, qualifying as clear context without 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?

No annotations are provided, so the description must disclose behavioral traits on its own. It only states 'trending products and news' without revealing output format, sorting, pagination, or whether the operation is read-only. This is insufficient for an agent to anticipate side effects or return structure.

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 with no redundant words, making it concise and easy to parse. It loses one point for being slightly too terse, as it could have used an explicit verb without compromising brevity.

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 there is no output schema and the description is minimal, the agent lacks critical context about the return value, result limits, or how 'trending' is determined. It also fails to differentiate from sibling tools that might overlap, making the description incomplete for confident tool selection.

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 provides 100% coverage for both parameters (lang with default and category with example values). The description adds no extra meaning beyond the schema, so the baseline score of 3 applies; the schema already handles parameter explanation.

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 'Trending products and news in a specific category' clearly states the tool's function: retrieving trending products/news filtered by category. It is distinct from siblings like product_search or price_comparison, though the phrasing is a noun phrase rather than an explicit verb, which slightly reduces clarity.

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 offers no guidance on when to use this tool versus alternatives such as product_search or price_comparison. It does not mention any exclusions, prerequisites, or typical use cases, leaving the agent without contextual decision support.

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 offers no information about what the tool actually does (e.g., pings the server, returns uptime, reports health metrics), any side effects, or response format. The description is purely a noun phrase with zero behavioral detail.

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 short with no wasted words, but it is a fragment rather than a complete sentence. It is concise but under-specified, lacking a clear verb or explanatory structure. It conveys just enough to hint at the purpose but not enough to be considered 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?

With no output schema and no annotations, the description should explain what a status check returns (e.g., JSON with uptime, health, version). It does not. For a simple health check tool, the description is incomplete and leaves the agent to guess the tool's behavior and output format.

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?

There are zero parameters, so the baseline score of 4 applies. The description does not need to explain parameter semantics, and it avoids adding any irrelevant parameter information.

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 identifies the resource (EcommerceOracle server) and the aspect (status), but lacks an explicit verb like 'check' or 'retrieve'. The action is implied rather than stated. It differentiates from siblings, which focus on ecommerce data, but the purpose is somewhat vague.

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 or when to prefer alternatives. There is no mention of intended use cases, such as verifying server availability before other operations, nor exclusions. The sparse phrase gives no practical direction.

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 only says 'monitor,' which suggests a non-destructive operation, but it doesn't mention what data is returned, whether it's real-time, if authentication is needed, or what 'presence' precisely means. This is minimal disclosure.

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 action ('Monitor') and resource ('marketplace presence and pricing news'), with no redundant words. It is concise and easy to parse, though its brevity contributes to the lack of detail in other dimensions.

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 absence of annotations, no output schema, and a relatively complex concept ('marketplace presence and pricing news'), the description falls short. It doesn't explain return values, limitations, or how this complements sibling tools like price_comparison or category_trends. A more complete description is needed for this 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 schema provides descriptions for all three parameters (lang, brand, competitor) with 100% coverage. The description's 'brand vs competitor' aligns with the brand and competitor params but adds no additional meaning about language or optionality. Baseline 3 is appropriate because the schema handles parameter semantics.

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 the specific verb 'Monitor' with the resource 'marketplace presence and pricing news' for brand vs competitor. It differentiates from siblings like price_comparison or product_search by emphasizing ongoing monitoring rather than a one-time lookup, though it doesn't explicitly name 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?

There is no explicit guidance on when to use this tool versus alternatives like price_comparison or category_trends. The description implies a monitoring use case but doesn't state exclusions or prerequisites, leaving the agent to infer 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, the description carries the full burden of behavioral disclosure. It reveals the output (merchant links) and the sources, but does not mention whether any network requests are made, how current the data is, or what happens if a product is unavailable. It is adequate but not deeply transparent.

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 exactly two sentences, front-loaded with the primary purpose and immediately followed by a concrete statement of what the user gets (merchant links). Every word 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?

The tool is simple: two well-described parameters and no output schema, but the description explains the core return value (merchant links) and examples. It could mention country-specific behavior or current availability, but it is sufficiently complete for a straightforward comparison 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 both 'query' and 'country' already have clear descriptions in the schema. The tool description adds no extra meaning to the parameters, 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 verb ('Compare'), resource ('product prices'), and scope ('across multiple sources'). It also names the specific merchants (Idealo, Amazon, Google Shopping), making it distinct from sibling tools like product_search or barcode_lookup.

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 price comparison but provides no explicit guidance on when to use this tool versus siblings such as product_search or category_trends. There are no exclusions or alternative recommendations, leaving the agent to infer the appropriate context.

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. It does not disclose what happens if the page lacks JSON-LD, whether the extraction fails gracefully, or any limitations on URL formats. The description is silent on error handling and edge cases, which is a significant gap for a tool reliant on a specific structured data format.

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 leads with the verb and resource, then provides the key data fields and the extraction mechanism. No filler or redundant 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?

With no output schema and no annotations, the description should convey what the agent can expect in return. It lists price, rating, and images, but does not explain the return format, success/failure behavior, or whether partial data is possible. The tool is simple, but the lack of behavioral details leaves some ambiguity.

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 only describes 'url' as a product page URL. The description adds critical semantics by stating the URL must contain JSON-LD and that extraction relies on it, which constrains the acceptable input beyond the schema's generic description. This adds value beyond the schema's parameter documentation.

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 extracts structured product data (price, rating, images) from a product page URL using JSON-LD. The verb 'extract' and resource 'product data' are specific, and the mention of JSON-LD distinguishes it from search or comparison tools.

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: 'from any product page URL' suggests it should be used when a specific product URL is available. However, it does not explicitly mention when not to use it or provide alternatives, such as product_search for discovery or barcode_lookup for barcode-based queries.

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 must fully disclose behavioral traits. It does state the return values (prices, brands, images), but omits important behaviors such as potential external API latency, error handling, and that results are aggregated from three sources with differing reliability. This leaves the agent with insufficient operational expectations.

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, front-loaded sentence with no wasted words. It conveys the core action, scope, and return fields efficiently, making it easy to parse.

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 a well-described schema but no output schema, the description gives a basic understanding. However, it does not clarify how results are aggregated, what 'source' values mean in practice, or how this tool relates to sibling tools like product_detail and price_comparison, leaving some contextual gaps.

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 already explains limit, query, and source. The description adds no additional parameter-specific meaning beyond what the schema provides, thus 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 uses a specific verb+resource ('Search products by name') and clearly distinguishes this tool from siblings by naming the multi-source scope (Algolia, UPCItemDB, Open Food Facts) and return fields (prices, brands, images). This makes its purpose 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?

The description implies usage for name-based product searches but provides no explicit guidance on when to choose this tool over alternatives like product_detail or price_comparison. It lacks exclusions or alternative recommendations, so the agent must infer usage from the tool name and siblings.

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 that the tool uses a public endpoint, implying no authentication is needed, but does not detail other behaviors like pagination, return format, or error cases. This is useful 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, front-loaded sentence with no wasted words. It immediately states the core purpose 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?

For a simple two-parameter fetch tool with no output schema, the description covers the essential context: what it fetches and from where. However, it does not specify that the response is a list of products, which would be helpful given the absence of an output schema.

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 provides full descriptions for both parameters (limit and domain), so the description adds no additional semantic value. Baseline of 3 is appropriate as the schema does the heavy lifting.

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 action ('Fetch products') and the resource ('any Shopify-powered store via their public /products.json endpoint'). It distinguishes from sibling tools like product_search and product_detail by emphasizing the generic fetch from any public Shopify store, though the differentiation could be more explicit.

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 explicit when-to-use or alternatives are provided. The description implies it is for fetching products from any Shopify store, but does not mention when to prefer product_search or product_detail, nor any prerequisites or exclusions.

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