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 that the tool returns a 'ranked list with prices, ratings, and reviews,' which adds some context about output behavior. However, it fails to disclose critical details such as rate limits, authentication requirements, data freshness, or error handling, which are essential for a tool interacting with external sources like Amazon or Google Shopping.

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 concise and front-loaded, with two sentences that efficiently convey the core functionality and use case. There is no unnecessary verbosity, and each sentence adds value: the first explains what the tool does, and the second suggests its application. However, it could be slightly improved by integrating parameter hints or sibling tool distinctions.

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 moderate complexity (4 parameters, no output schema, no annotations), the description is partially complete. It covers the basic purpose and output format but lacks details on behavioral aspects like rate limits or error handling. Without an output schema, it should ideally explain the return structure more thoroughly, but it only mentions 'ranked list with prices, ratings, and reviews,' which is somewhat vague. The description is adequate but has clear gaps in context.

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, providing clear details for all four parameters (limit, source, country, category). The description does not add any additional semantic information beyond what the schema already specifies, such as explaining the significance of 'country' or 'category' choices. Since schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

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 specifies the verb ('Get'), resource ('top-selling products'), and scope ('Amazon or Google Shopping'), but does not explicitly differentiate it from sibling tools like 'product_search' or 'market_position', which might 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 usage context with 'Great for market research,' suggesting it's suited for market analysis. However, it lacks explicit guidance on when to use this tool versus alternatives like 'product_search' or 'compare_prices,' and does not mention any prerequisites or exclusions, leaving room for ambiguity.

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 mentions the three platforms and output metrics but doesn't cover critical behaviors like rate limits, authentication needs, data freshness, error handling, or whether this is a read-only operation. For a tool querying external APIs, this leaves significant gaps.

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 efficiently structured in two sentences: one stating the purpose and scope, and another detailing the return values. It's front-loaded with the core function and avoids unnecessary elaboration, though it could be slightly more concise by integrating the output details more seamlessly.

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 complexity of querying multiple external APIs and no output schema, the description is incomplete. It lists output metrics but doesn't specify data formats, units (e.g., currency), or structure (e.g., JSON keys). With no annotations to cover behavioral aspects, this leaves the agent under-informed for effective tool invocation.

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 (limit, query, country) with their types and defaults. The description adds no additional parameter semantics beyond what's in the schema, such as explaining query format examples or country code constraints, meeting the baseline for high schema coverage.

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 specific action ('compare prices'), resources ('Google Shopping, Amazon, and eBay'), and output ('best price, worst price, potential savings, and a ranked price list'). It distinguishes itself from siblings like price_history (historical data) or product_search (finding products) by focusing on cross-platform price comparison.

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 like competitor_pricing, market_position, or track_price. It doesn't mention prerequisites, exclusions, or specific scenarios where this comparison is most appropriate, leaving the agent to infer usage from the purpose alone.

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 burden for behavioral disclosure. It describes what the tool does (comparison with specific outputs) but lacks critical behavioral details: whether this is a read-only operation, if it requires authentication, rate limits, data freshness, or error conditions. The description doesn't contradict annotations (none exist), but provides minimal behavioral context beyond basic functionality.

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 perfectly concise with two sentences that each earn their place. The first sentence establishes the core functionality, and the second specifies the output details. No wasted words, and the most important information (what the tool does) is front-loaded.

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 and no output schema, the description provides adequate basic functionality but lacks completeness for a comparison tool. It explains what the tool returns (cheaper product, price difference, offers) but doesn't cover output format, error handling, or limitations. For a tool with 3 parameters and no structured behavioral hints, the description should do more to compensate.

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 with their types and basic descriptions. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., format examples, constraints, or relationships between product_a and product_b). Baseline 3 is appropriate when 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 with specific verbs ('compare pricing', 'shows') and resources ('two specific products or brands', 'across all marketplaces'). It distinguishes from siblings like 'compare_prices' by specifying side-by-side comparison of exactly two products with detailed output metrics (cheaper product, price difference, offers).

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 context (comparing two specific products/brands for pricing analysis) but doesn't explicitly state when to use this tool versus alternatives like 'compare_prices' or 'product_search'. No exclusions or prerequisites are mentioned, leaving the agent to infer appropriate scenarios from the purpose statement alone.

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 burden but offers minimal behavioral insight. It mentions what information is returned but doesn't disclose whether this is a read-only operation, if it requires authentication, has rate limits, affects system state, or provides real-time versus cached data. The list format suggests a diagnostic/reporting function but lacks operational details.

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 comma-separated list without clear structure or prioritization. While concise in word count, it's not well-organized - it mixes operational status (health, connectivity) with configuration data (sources, countries, tools) and business information (pricing). A more structured approach would improve clarity.

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 diagnostic/status tool with no annotations and no output schema, the description is incomplete. It lists information categories but doesn't explain the format, depth, or reliability of the data returned. Given the complexity implied by covering server health through pricing info, more guidance about what to expect from this multi-faceted tool would be helpful.

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 with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist, which is correct for a parameterless tool. Baseline for 0 parameters is 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 lists multiple functions (server health, API connectivity, supported sources/countries, tool list, pricing info) but doesn't specify a clear verb or primary action. It's a collection of status checks rather than a focused purpose statement, making it somewhat vague about what exactly the tool does beyond 'provides various system information'.

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 about when to use this tool versus the 10 sibling tools. The description doesn't indicate whether this is for diagnostics, initial setup, monitoring, or comparison with other tools. There's no mention of prerequisites, timing, or alternatives.

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. While it mentions what the analysis includes (price positioning, rating comparison, alternatives), it doesn't describe how the analysis is performed, what data sources are used, whether it requires authentication, rate limits, or what the output format looks like. For an analysis tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

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 efficiently structured in a single sentence that packs substantial information about what the analysis includes. It's appropriately concise without being overly brief, though it could potentially benefit from slightly more structure (e.g., separating the analysis components with commas or bullet points for clarity).

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 an analysis tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the output contains, how comprehensive the analysis is, what data sources are used, or any limitations. The description should provide more context about the scope and nature of the market position analysis to compensate for the lack of structured metadata.

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 (query, country, category) with their types and descriptions. The description doesn't add any additional parameter semantics beyond what's in the schema - it doesn't explain how the 'query' parameter should be formatted, what country codes are supported, or how categories are derived. Baseline 3 is appropriate when 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: analyzing a product's market position across price positioning, rating comparison, and alternatives. It specifies the verb 'analyze' and resource 'product' with concrete dimensions. However, it doesn't explicitly distinguish this from sibling tools like 'compare_prices' or 'competitor_pricing' which might overlap in 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 provides no guidance on when to use this tool versus alternatives. With sibling tools like 'compare_prices', 'competitor_pricing', and 'review_summary' that might handle similar analyses, there's no indication of when this specific market position analysis is preferred or what unique value it offers compared to those alternatives.

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 describes the four actions but lacks details on permissions, rate limits, notification mechanisms, or what happens during 'delete' (e.g., irreversible). This is a significant gap for a tool with multiple operations including destructive ones.

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 appropriately sized and front-loaded, with the first sentence stating the core purpose and the second explaining the benefit. The list of actions is concise and necessary for clarity. Every sentence earns its place 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?

Given the tool's moderate complexity (5 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and actions well but lacks behavioral details like error handling or return formats, which are crucial for a tool with multiple operations including destructive ones.

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 five parameters. The description adds minimal value by listing the four actions, which aligns with the 'action' parameter's description, but doesn't provide additional syntax, format details, or clarify dependencies like 'target_price' being required for 'set' beyond what the schema states.

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 specific verbs ('Set, check, list, or delete') and resource ('price alerts'), distinguishing it from sibling tools like 'track_price' or 'price_history' by focusing on alert management rather than tracking or 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 clear context for when to use this tool ('Get notified when a product drops below your target price') and lists the four specific actions, but it doesn't explicitly state when not to use it or mention alternatives among sibling tools like 'track_price' for continuous monitoring.

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 burden for behavioral disclosure. It describes what the tool does (viewing price history with trend analysis) and mentions tracking metrics, but doesn't cover important behavioral aspects like whether this is a read-only operation, what format the output takes, whether there are rate limits, or authentication requirements. The description adds some value but leaves significant gaps.

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 with just two sentences that each earn their place. The first sentence explains the core functionality, and the second provides crucial prerequisite information. There's zero wasted text and the information is front-loaded appropriately.

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 3 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It explains the purpose and prerequisite clearly, but doesn't address output format, error conditions, or how the trend analysis is presented. Given the complexity of price history analysis, more context about what the agent can expect would be helpful.

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?

With 100% schema description coverage, the schema already documents all three parameters thoroughly. The description doesn't add any additional parameter semantics beyond what's in the schema - it doesn't explain how 'asin' and 'query' interact, when to use one versus the other, or provide context about the 'limit' parameter's practical implications. This meets the baseline for high schema coverage.

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 specific verbs ('view stored price history', 'tracks min, max, average prices') and resources ('product'), and explicitly distinguishes it from sibling 'track_price' by noting that tool must be called first to build history data. This provides excellent differentiation from other pricing-related siblings.

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 explicit usage guidance by stating 'Call track_price first to build up history data', which clearly indicates a prerequisite relationship with a specific sibling tool. This gives the agent clear direction on when this tool can be effectively used versus when it would be inappropriate.

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. It discloses key behavioral traits: multi-source search, return data types (prices, ratings, links), and support for country-specific results and sorting. However, it lacks details on rate limits, authentication needs, error handling, or pagination, leaving gaps for a tool with 5 parameters.

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 front-loaded with core functionality in the first sentence, followed by additional features. Every sentence adds value: the first defines purpose, the second specifies return data and key capabilities. No wasted words, efficiently 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 search tool with 5 parameters, no annotations, and no output schema, the description is adequate but incomplete. It covers what the tool does and key features, but lacks details on output format, error cases, or limitations (e.g., result freshness, source availability). Given the complexity, more context would be helpful.

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 fully documents all 5 parameters. The description adds minimal value beyond the schema, mentioning country codes and sorting options but not elaborating on syntax or format. Baseline 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 ('Search for products'), resources ('across Google Shopping, Amazon, and eBay'), and scope ('returns prices, ratings, links'), distinguishing it from siblings like 'price_history' or 'review_summary' that focus on specific aspects rather than comprehensive 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 implies usage for broad product searches across multiple marketplaces, but does not explicitly state when to use alternatives like 'compare_prices' or 'bestseller_list'. It provides clear context (country-specific results, sorting) but lacks explicit exclusions or comparisons to sibling tools.

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 mentions the tool's usefulness but doesn't describe behavioral traits such as rate limits, authentication needs, error handling, or what happens if multiple parameters are provided (e.g., 'asin' vs. 'query'). This leaves significant gaps for an AI agent to understand how to invoke it correctly.

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 appropriately sized and front-loaded: the first sentence states the core purpose, and the second sentence adds context. Every sentence earns its place with no wasted words, making it efficient and 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?

Given the tool's complexity (3 parameters, no output schema, no annotations), the description is minimally complete. It covers the purpose and usage context but lacks details on behavior, parameter interactions, and output format. Without annotations or output schema, more information would be helpful for the agent to use it effectively.

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 ('asin', 'query', 'country') with descriptions. The description doesn't add any meaning beyond what the schema provides, such as explaining the relationship between 'asin' and 'query' or clarifying the default 'country' value. Baseline 3 is appropriate when 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: 'Get review ratings, score breakdown, and top customer reviews for a product from Amazon.' It specifies the verb ('Get') and resource ('review ratings, score breakdown, and top customer reviews'), but doesn't explicitly differentiate it from sibling tools like 'product_search' or 'market_position' that might also involve product information.

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 implied usage context: 'Useful for purchase decisions and product quality assessment.' This suggests when to use the tool, but it doesn't explicitly state when not to use it or name alternatives among the sibling tools (e.g., 'product_search' for broader product info). No exclusions or prerequisites are mentioned.

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 mentions what information is shown but doesn't cover critical aspects like whether this is a read-only operation (implied but not stated), rate limits, authentication needs, data freshness, or potential costs. For a tool querying external APIs like Amazon and Google Shopping, this lack of behavioral context is a significant gap.

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 perfectly concise and well-structured in a single sentence that efficiently communicates the core functionality. Every word earns its place by specifying the platforms (Amazon and Google Shopping), the action (check availability and stock status), and the specific information returned. There's no wasted verbiage 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?

Given the tool's moderate complexity (querying two major e-commerce platforms) and the absence of both annotations and output schema, the description is minimally adequate. It covers what the tool does but lacks crucial context about behavioral aspects, return format, error handling, and differentiation from siblings. The 100% schema coverage helps, but for a tool without output schema, more detail about what gets returned would be beneficial.

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, providing clear documentation for all three parameters (asin, query, country). The description adds no additional parameter semantics beyond what's in the schema. It doesn't explain the relationship between asin and query parameters, or provide examples of valid country codes beyond the default 'DE'. Baseline score of 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: checking product availability and stock status across Amazon and Google Shopping, with specific details about what information it provides (in-stock/out-of-stock, prices, delivery info, sellers). It distinguishes itself from siblings like 'price_history' or 'review_summary' by focusing on real-time availability rather than historical or qualitative data. However, it doesn't explicitly differentiate from 'product_search' which might overlap in 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 provides no guidance on when to use this tool versus alternatives like 'product_search', 'compare_prices', or 'track_price'. It doesn't mention prerequisites, such as needing either an ASIN or query parameter, or when one might be preferred over the other. There's no indication of use cases or limitations compared to sibling tools.

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 mentions 'build price history over time', implying ongoing or repeated tracking, but does not specify how this works (e.g., frequency, storage, or whether it triggers alerts). It lacks details on permissions, rate limits, or what the tool returns, leaving significant gaps for a tool that involves data collection over time.

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 efficiently structured in two sentences: the first states the core purpose, and the second provides parameter-specific usage guidance. Every sentence adds value without redundancy, making it front-loaded and easy to parse. There is no wasted text or unnecessary elaboration.

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 complexity of tracking and building history over time, no annotations, and no output schema, the description is incomplete. It does not explain what the tool returns (e.g., current price, tracking confirmation, or historical data), how the history is built or accessed, or any behavioral aspects like persistence or alerts. For a tool with these characteristics, more context is needed to be fully useful.

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. The description adds marginal value by reinforcing the use cases for 'asin' and 'query', but does not provide additional syntax, format details, or constraints beyond what the schema states. The baseline score of 3 is appropriate as the schema handles most of the 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's purpose with specific verbs ('track', 'build') and resources ('current price', 'price history'), and distinguishes it from siblings like 'price_history' (which likely shows existing history) by emphasizing ongoing tracking and history building. It explicitly mentions two distinct use cases: exact product tracking via ASIN and best-price discovery via search query.

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 on when to use each parameter (ASIN for exact tracking, query for best-price discovery), but does not explicitly state when to use this tool versus alternatives like 'price_history' or 'compare_prices'. It offers practical guidance without exclusions or prerequisites, falling short of naming specific sibling tools as alternatives.

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