priceoracle

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 implies a read-only search but fails to mention external dependencies, rate limits, or return format. The term 'search' hints at non-destructive behavior, but no explicit disclosure is made.

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 sentence with zero waste, efficiently conveying the core purpose. It earns a high score for conciseness, though it lacks additional structure like usage hints.

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 no annotations, no output schema, and sibling tools present, the description is incomplete. It doesn't explain when to use this tool over others or what the response might look like, leaving significant gaps for an 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?

Schema coverage is 100% with both parameters described. The description adds minimal value beyond the schema, only mentioning 'prices and product info' which loosely relates to the query parameter.

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 searches Amazon/open product database for prices and product info, providing a specific verb and resource. It distinguishes from siblings like barcode_lookup and price_history, though it doesn't explicitly call out 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?

No guidance on when to use this tool vs alternatives. It simply states the action without contextual cues or comparisons to sibling tools such as competitor_prices or price_search.

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 behavior. It states 'look up', implying a read-only operation, but gives no details on response format, error handling, or data source, leaving ambiguity about what 'details' includes.

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?

One short sentence conveys the core function without waste, which is excellent for a simple tool.

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 one parameter, the description is minimally adequate but lacks mention of output shape, pricing currency, or fallback behavior when a barcode isn't found.

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 'barcode' is already fully described in the schema (EAN/UPC example), and the description adds no additional semantics beyond restating that context, so baseline score 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 the specific verb 'look up' with the resource 'product details and prices' and clearly identifies the input mechanism (EAN/UPC barcode), which distinguishes it from sibling search tools that rely on text queries.

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 use when a barcode is available, but it does not explicitly state when to favor this over alternatives like price_search or competitor_prices, nor does it provide 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 full burden of behavioral disclosure, but it only states the purpose. It does not indicate whether the operation is read-only, what kind of output to expect, whether auth is needed, or any limitations. This leaves key behavioral traits unaddressed.

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 that efficiently conveys the core function without redundant words. It earns its place and is appropriately concise for the tool's simple parameter set.

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 lacks an output schema and annotations, so the description must compensate by explaining expected results, but it only states the comparison purpose. It does not describe the return format, how 'price positioning' is measured, or what 'news coverage' entails, leaving the agent uncertain about the tool's operation.

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 each parameter (brand, competitors, product_type) having a clear description. The tool description itself adds no additional parameter semantics, so the baseline of 3 is appropriate given the schema's completeness.

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: comparing the brand's price positioning against competitors specifically in news coverage. It uses a specific verb ('Compare') and resource, which distinguishes it from sibling tools like price_search or price_history that focus on individual price lookups or trends.

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 use case (comparing brand vs. competitor prices in news coverage) but does not explicitly state when to use this tool versus alternatives. It provides no exclusions, prerequisites, or named sibling alternatives, leaving the agent to infer selection criteria.

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 only says 'server status' and does not mention whether the tool is read-only, what it returns (e.g., UP/DOWN, latency, HTTP code), or how it handles errors. The read-only nature is implied but never stated.

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 at four words, with no wasted text. It is front-loaded with the resource and meaning. However, it is a fragment rather than a complete sentence, and could be slightly clearer with a verb, but it 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 0-parameter tool, the description is minimally acceptable. However, with no output schema, it leaves the return format unspecified. An agent cannot predict whether the output is a string, JSON, or a status code, nor what failure looks like. It is adequate but has clear gaps in completeness.

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 0 parameters and an empty input schema, so per the rubric the baseline is 4. The description adds no parameter information, but none is needed. Schema coverage is trivially 100%, so the description does not need to compensate.

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 (PriceOracle server) and the property (status), making the tool's purpose clear. It distinguishes itself from sibling price-search tools by focusing on server health. However, it lacks an explicit verb like 'check' or 'get', so it's slightly telegraphic.

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. There are no exclusions, prerequisites, or context such as 'run before other queries to verify server availability.' The description only states what the tool is about, not how or when 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 provided, the description carries full responsibility for behavioral disclosure. It only says 'Search Idealo price comparison' without revealing what kind of results are returned, whether multiple results appear, pagination, rate limits, or any other behavioral traits. This is a significant gap for an unannotated tool.

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, focused sentence with no wasted words. It is highly concise and front-loaded, every word contributes to conveying the tool's purpose.

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 an output schema and annotations, the description is incomplete. It does not specify what the search returns, how results are structured, or any usage context beyond the basic query. For a tool this simple, more explanatory detail is needed to make it self-sufficient.

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 description covers 100% of the single parameter, providing examples for 'query'. The description adds no additional meaning beyond the schema, 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's function: searching Idealo price comparison for a product. However, it does not explicitly distinguish itself from sibling tools like price_search or price_history, so it lacks the differentiation needed for a 5.

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: use this when you want to search Idealo for product prices. But it provides no explicit guidance on when to prefer this over alternatives like amazon_search or price_search, nor any exclusions, so it stops short of full clarity.

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 only states the basic action without disclosing output format, data source, latency, or any limitations. It does not explicitly confirm side-effect-free behavior or explain what constitutes 'recent' or 'news'.

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, clear sentence with no redundant wording. It is appropriately sized for a simple tool with one parameter.

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?

There is no output schema, so the description should explain what the tool returns, but it only says 'news' without structure or format. The ambiguity around the required status of 'product' (required parameters: 0) further reduces completeness. The description is too thin for an agent to fully rely on.

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 already provides full coverage for the 'product' parameter with an example and guidance. The description adds no additional semantic information, so the baseline of 3 is appropriate. Note that the parameter is not marked required, which is a subtle gap not addressed by the description.

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 'Find' and the resource 'recent price change news' for a product. It distinguishes itself from sibling tools like price_search by focusing on news about changes rather than current prices.

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 when one needs recent price change news, but it does not explicitly mention when not to use it or how it compares to alternatives like price_search or competitor_prices. No exclusions or alternative tool names are provided.

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 only states that it searches prices, but does not disclose whether it returns live data, how it aggregates from 'news and price comparison sites,' or any limitations (e.g., currency handling, result format). This is minimal and lacks 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 a single, concise sentence that communicates the core purpose without filler. It is front-loaded with the verb and resource, and every word adds value. This meets the standard for exemplary conciseness.

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 there is no output schema, the description should explain what the search returns (e.g., price list, normalized results, comparisons). It does not. The description also does not clarify how it differs from alternatives like competitor_prices or idealo_search, leaving ambiguity in context. For a low-complexity tool, this is acceptable but still incomplete.

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 three parameters (product, category, currency). The description adds no extra meaning beyond the schema, such as how to combine product and category or the default currency behavior. Baseline 3 is appropriate since 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 tool's purpose: searching product prices from news and price comparison sites. It uses a specific verb ('search'), a resource ('product prices'), and identifies the data sources, which distinguishes it from sibling tools like amazon_search (specific retailer) and price_history (historical 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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention whether it should be preferred over competitor_prices or idealo_search, nor does it include any exclusions or prerequisites. Usage context is entirely absent.

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