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

No annotations are provided, so the description carries the full burden. 'Look up information' implies a read operation, but it does not disclose any details such as whether price history is included by default, what 'information' includes, or any constraints beyond the ASIN count. This is a minimal but not transparent description for a data retrieval 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, concise sentence that front-loads the core purpose and includes a key constraint (up to 100). There is no redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and the fact that the schema fully explains parameters, the description is sufficient for an agent to understand the tool's function. It differentiates from the singular product lookup sibling and includes the batch limit. A lack of return-format guidance is acceptable since no output schema exists and the description needn't explain return values.

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 all four parameters (days, asins, domain, history) having clear descriptions. The tool description itself adds no parameter-level detail beyond what the schema 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 uses a specific verb ('Look up') and resource ('Amazon products by ASIN') and clearly differentiates from sibling keepa_product_lookup by emphasizing 'multiple' and 'up to 100'. This makes the 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 when to use this tool: when you need to look up multiple products at once (up to 100). However, it does not explicitly mention alternatives or when not to use it (e.g., for a single ASIN, use keepa_product_lookup), 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 full burden of behavioral disclosure. It only states the basic action without revealing pagination behavior, domain handling, or response format. This is a significant gap for a tool with multiple 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 a single, focused sentence with no redundant wording. It front-loads the action and resource, making it immediately scannable and appropriately concise for the tool's simplicity.

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?

Without an output schema or annotations, the description needs to explain return values and edge behaviors, but it only provides a high-level summary. The lack of detail on pagination, domain specifics, and expected output makes it incomplete for an agent to fully leverage the 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 already documents all three parameters (category, page, domain) with descriptions, achieving 100% coverage. The description adds no additional meaning beyond labeling category as 'specific', so it does not compensate beyond the schema baseline.

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 ('Get') and the resource ('best sellers list for a specific Amazon category'), making the tool's purpose obvious. It does not explicitly differentiate from sibling tools like keepa_product_finder, but the specificity of 'best sellers list' implies a distinct use case.

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 scenarios, prerequisites, or when not to use it, leaving the agent to infer usage from the tool name and schema.

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 responsibility. It states 'comprehensive category analysis' but never discloses whether this is read-only, what data it returns, any limitations, or side effects. The description lacks essential 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 a single concise sentence that front-loads the main purpose. It avoids unnecessary filler, though the information provided is somewhat generic. It's appropriately brief.

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 with 7 parameters, enums, and no output schema, the description is insufficient. It doesn't explain what the analysis output looks like, how parameters interact, or any usage context. This is a significant gap for a tool with such rich configuration.

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 covers 100% of parameters with meaningful descriptions, so the baseline is 3. The tool description does not add parameter-specific details beyond the schema, nor does it clarify relationships between parameters. It's acceptable but not enhancing.

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 identifies the tool as a comprehensive category analysis for finding best products, opportunities, and market insights. It uses specific verbs and a resource, though it doesn't explicitly distinguish from sibling tools like best_sellers or product_finder. Overall, it's clear but somewhat generic.

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 doesn't mention any prerequisites, exclusions, or scenarios where another tool would be appropriate. This leaves the agent without clear decision 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 provided, the description carries the full burden of behavioral disclosure. It lists analysis outputs but does not disclose whether it triggers any side effects, what inputs are required, how results are returned, or any rate limits. For a read-only analysis tool, this is minimal; it adds no context beyond the core function.

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 front-loads the core purpose and lists key outputs. It is efficient and free of unnecessary filler, though it could be more specific about expected inputs or output format to earn a 5.

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 has 6 optional parameters, no output schema, and no annotations, the description should provide more context about how to configure the analysis (e.g., when to supply ASINs vs categoryId, how analysisType changes behavior). The current description is too thin to fully guide an agent on tool invocation, especially with sibling tools offering overlapping capabilities.

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 documents all six parameters with descriptions, achieving 100% coverage. The description adds marginal semantic value by aligning analysisType values (fast_movers, slow_movers, etc.) with its listed outputs, but it does not explain parameter relationships or usage details beyond what the schema provides. This meets the baseline for full 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 performs 'comprehensive inventory analysis' and explicitly enumerates the types of insights it provides (fast movers, slow movers, stockout risks, seasonal patterns). This distinguishes it from sibling tools like keepa_category_analysis or keepa_sales_velocity by focusing on inventory-specific analytics.

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 guidance is given regarding when to use this tool over alternatives. The description does not mention scenarios, prerequisites (e.g., needing ASINs or category IDs), or exclusions. While the title and description imply inventory analysis, there is no direct comparison 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, the description must disclose behavior. 'Get historical price data' implies a read-only operation, but it doesn't state whether it is safe, if it consumes API tokens, how many data points are returned, or any rate limit implications. This is a significant gap for a tool that likely interacts with an external API.

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 of eight words. It is front-loaded with the action and object, contains no filler, and 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?

Given the tool's complexity (4 parameters, no output schema, no annotations), the description is far too minimal. It fails to mention output format, parameter relationships, or how it fits among siblings. A complete description should at least note that it returns time-series data or that it uses the Keepa API token.

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, so the baseline is 3. The tool description adds no additional meaning beyond the schema, such as how parameters interact (e.g., dataType values) or what the response contains. It neither improves nor degrades 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 retrieves historical price data for an Amazon product, using the specific verb 'Get' and a specific resource. It distinguishes from siblings like keepa_product_lookup (current data) and keepa_sales_velocity (sales trends), though it does not explicitly name these distinctions.

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 siblings, no context on use cases, and no exclusions. It simply states what the tool does, leaving the agent to infer its proper role among the ten 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 only says it finds products with filtering, but gives no information about return format, side effects, authentication, or whether it is a read-only operation. The description adds no behavioral details beyond the purpose statement.

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, front-loaded with the core purpose ('Advanced product finder with filtering'), and contains no unnecessary words. It is concise and well-structured for quick comprehension.

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 19 parameters and no output schema, the description is severely underspecified. It does not explain what the tool returns, how pagination works, how filters interact, or any default behaviors. The schema covers parameter details, but the overall tool context is lacking.

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 has 100% coverage for all 19 parameters with descriptions, so the baseline is 3. The description adds a high-level grouping of filter dimensions (rating, price, sales, competition) that maps to the relevant parameters, but it does not add significant meaning beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool finds products with filtering similar to Keepa Product Finder, specifying dimensions like rating, price, sales, and competition level. This distinguishes it from siblings like keepa_product_lookup or keepa_best_sellers.

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 product discovery with filtering, but it does not explicitly say when to use this over alternatives like keepa_search_deals or keepa_product_lookup. No exclusions or comparisons to siblings are provided, though the 'similar to Keepa Product Finder' gives some 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?

With no annotations, the description carries the burden of disclosing safety and side effects. It says 'look up' (suggesting read-only) but does not mention potential token consumption, rate limits, error behavior, or details about the response. The vague 'detailed information' leaves much undisclosed.

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, front-loaded sentence with no filler. It communicates the core purpose effectively and is appropriately concise.

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 7 parameters, no output schema, and no annotations, the description is too thin. It omits return value details, typical use cases, and any limitations. The agent cannot fully gauge what 'detailed information' includes or how to handle 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?

Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-level meaning beyond the schema's existing descriptions, which are already clear.

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 ('look up') and resource ('detailed information for a single Amazon product by ASIN'). It clearly distinguishes from siblings like batch lookup (multiple products) and price history (history only).

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 phrase 'single Amazon product' implies the tool is for one ASIN, but there is no explicit guidance on when to use this vs. alternatives like keepa_batch_product_lookup or keepa_price_history. No exclusions or alternative names 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?

There are no annotations, so the description must disclose operational behavior. It fails to mention that the tool accepts multiple ASINs, supports pagination and filtering, or what the output looks like. The description is outcome-oriented but lacks the operational details an agent needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, focused sentence that front-loads the purpose and includes a helpful application phrase. No wasted words or redundant 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?

With 14 parameters, no output schema, and no annotations, the description only provides a high-level purpose. An agent receives no guidance on what inputs to prioritize, what the result will look like, or how to interpret the output, making it incomplete for this complexity.

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 14 parameters (100% coverage), so the baseline is 3. The tool description adds no extra parameter semantics beyond what the schema already provides.

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 ('Analyze') with a specific resource ('sales velocity and inventory turnover') and adds an application context ('find products that sell quickly and avoid slow movers'). This clearly distinguishes it from sibling tools like price history or product 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 a use case (analyze velocity/turnover to identify fast-selling products) but does not explicitly state when to use this tool over alternatives such as product_finder or inventory_analysis, nor does it mention 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.

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 mentions 'current deals' and 'filtering options' but discloses no return format, pagination behavior, or any other operational traits such as data availability or result ordering, which are not already visible in 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 a single sentence with minimal waste, front-loading the core purpose. The phrase 'with filtering options' is slightly redundant given the schema but does not significantly detract from the 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?

For a tool with 10 parameters, no output schema, and no annotations, the description is too sparse to understand return values, typical use cases, or expected behavior. It lacks the context needed to guide an agent effectively beyond the parameter list, making it insufficient for a tool of this complexity.

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 describes all 10 parameters with clear descriptions (100% coverage), so the description's generic 'filtering options' adds little value. It does not introduce any parameter semantics beyond what the schema already provides, matching 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 action (search) and resource (Amazon deals), with 'current' indicating time-sensitive content. It is distinguishable from siblings like keepa_best_sellers and keepa_price_history by focusing on deals rather than historical data or best-seller lists, though it does not explicitly contrast them.

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 scenarios or exclusions, and there is no differentiation from siblings like keepa_product_finder, leaving the agent without criteria for tool selection.

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 must disclose behavioral traits. It only says 'Get detailed information', which is generic and does not clarify whether the operation is read-only, any rate limits, possible errors, or what data is returned. No additional context is provided beyond the basic action.

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 with no unnecessary words. It is front-loaded with the primary action and resource. While it could be more informative, it is not verbose or wasteful.

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 has three parameters and no output schema, so the description should explain what kind of seller information is returned and in what form. The current description is too vague and does not provide enough context for an agent to set expectations or decide between related tools.

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 comprehensive descriptions for all three parameters (domain, seller, storefront), achieving 100% coverage. The description adds no extra parameter meaning, but the baseline of 3 is appropriate since 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 clearly states the action ('Get') and the resource ('detailed information about an Amazon seller'), which distinguishes it from product-related lookups. However, it does not specify what constitutes 'detailed information' or explicitly compare with sibling tools, leaving some ambiguity.

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 like keepa_product_lookup or keepa_sales_velocity. There are no described use cases, prerequisites, or exclusions, leaving the agent to infer applicability.

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 exist, so the description carries the full burden. It indicates a read operation with 'Check,' but does not disclose whether checking consumes tokens, what exactly 'account status' includes, or any other behavioral traits.

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 8-word sentence, immediately front-loading the core function with 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?

For a parameterless tool with no output schema, the description states the two things it checks (tokens and account status), sufficient for invocation. However, it leaves the exact return format and any prerequisites unspecified, slightly reducing 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 input schema is empty (0 parameters), and the description correctly avoids parameter details. Baseline for 0 params 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 uses the specific verb 'Check' with the resource 'remaining Keepa API tokens and account status,' clearly distinguishing it from sibling tools that handle product data, price history, etc.

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 alternative guidance is provided. The use case (pre-call token check) is implied but not stated, so it scores as implied usage.

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