Lumnix

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

No annotations provided; description does not disclose behavioral traits such as side effects, authentication needs, rate limits, or data freshness. Only describes output content.

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?

Single sentence, front-loaded verb and purpose, no redundant information. Every word adds value.

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 simple input (one required parameter), no output schema, and no annotations, the description is reasonably complete by listing what the response includes. Could mention that it's a read-only operation, but still adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema covers the single parameter with a basic description. The tool's description does not add meaning beyond 'product_id' being an Alibaba product ID, but schema coverage is 100%, so baseline 3 is appropriate.

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

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

Description clearly states the tool retrieves detailed product info including specific categories like pricing tiers, specs, variants, supplier info, and certifications, distinguishing it from search or matching tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus siblings (e.g., alibaba_search_products for search, alibaba_match_to_amazon for matching). Lacks context on prerequisites 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 must disclose behavioral traits. It describes the matching function but does not mention side effects (likely read-only), rate limits, data freshness, or authentication 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?

Two sentences, each purposeful and front-loaded. No unnecessary words or redundancy.

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

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

The tool has 4 parameters, no output schema, and no annotations. The description covers the core purpose but could be more complete by hinting at output format (e.g., matched products with pricing and margins) or limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for all 4 parameters, so the description adds minimal value beyond the schema. The description does not elaborate on how parameters are used.

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 'Match' and the resources 'Alibaba products to Amazon listings', with a specific purpose 'to compare pricing and calculate margins'. This distinguishes it from sibling search or detail retrieval tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for finding the same product on Amazon for cost comparison, but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention 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, the description should disclose side effects and constraints. It mentions return types but no details on authentication, rate limits, or whether it can modify data. The tool is read-only by nature, so a '3' is reasonable.

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?

Single sentence, 15 words, front-loaded with the verb 'Search'. Every word is necessary.

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 presence of a detailed input schema and no output schema, the description adequately covers the tool's purpose and output. Could mention sorting or error handling, but not essential.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage, so the schema already documents parameters. The description adds no additional semantic context beyond stating what is returned.

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 searches Alibaba for suppliers and products and specifies what it returns (product details, MOQs, prices, supplier ratings). It differentiates from sibling tools by naming Alibaba.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied (e.g., when needing to find suppliers on Alibaba), but there is no explicit guidance on when to use this tool versus alternatives or when not to use it.

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

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

No annotations, so description bears full burden. It discloses what the tool returns, implying read-only behavior. It could explicitly state safety, but the return-based description is adequate for a vetting 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?

Two succinct sentences with no fluff, front-loaded with the core action and key outputs.

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 tool with one param and no output schema, the description fully covers the purpose and return values, making it actionable without further elaboration.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for the single param, so baseline is 3. The description does not add further semantic detail beyond what the schema 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's action ('Vet an Alibaba supplier') and lists specific return values (trust score, years in business, etc.), making it distinct from sibling tools like alibaba_get_product_details or alibaba_search_products.

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 needing to assess supplier credibility, but lacks explicit guidance on when not to use it or alternatives. However, the context is clear enough given the sibling tool names.

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 present, so the description carries the burden of disclosing behavioral traits. It implies a read-only operation by stating it 'scores' a supplier and returns metrics, but does not explicitly confirm safety or potential side effects. The description adds limited behavioral context beyond the listed outputs.

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 effectively conveys the tool's purpose and outputs with no redundancy or filler. It front-loads the core action ('Score an AliExpress supplier') and then lists key deliverables.

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 one required parameter, no output schema, and no annotations, the description provides a reasonable overview by listing the returned metrics. However, it does not specify the format or structure of the output, which would be helpful for an agent to interpret results.

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 one parameter (product_id) already described in the schema as 'Product ID (extracts supplier from product).' The description reinforces that the supplier is derived from the product ID, adding marginal value. However, no additional parameter constraints or format guidance are provided, so the description does not significantly enhance the schema.

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

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

The description clearly states the tool scores an AliExpress supplier and lists specific return metrics (trust metrics, response rates, dispute rates, reliability assessment). It distinguishes itself from sibling tools like alibaba_vet_supplier by targeting the AliExpress platform and focusing on supplier scoring rather than product details or 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 provides no guidance on when to use this tool versus alternatives (e.g., alibaba_vet_supplier for Alibaba, or other analysis tools). It does not mention prerequisites, exclusion criteria, or the context in which this tool is appropriate.

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

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It indicates the tool returns product info, but does not mention side effects, rate limits, authentication needs, or error conditions. While it states the read-like nature, it lacks necessary transparency for an agent.

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

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

The description is a single, well-front-loaded sentence. Every phrase adds value: 'Get detailed AliExpress product info' states purpose, and the list of included data types ('variants, pricing tiers, reviews, and shipping options') provides concrete scope. No wasted words.

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 (single parameter, no output schema, low complexity), the description is mostly complete. It specifies the core functionality and key data. Minor gaps: does not note that product_id must be valid or that this is a read-only operation, but overall adequate for a basic lookup tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% (product_id described as 'AliExpress product ID'). The tool description does not add new parameter-level meaning; it only lists output fields. Baseline score of 3 is appropriate since schema already documents the single required parameter sufficiently.

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: to get detailed AliExpress product info. It specifies the verb 'Get' and the resource 'detailed AliExpress product info', and lists included data types (variants, pricing tiers, reviews, shipping options), distinguishing it from sibling tools like aliexpress_search_products that search rather than retrieve details.

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 explicit guidance on when to use this tool vs alternatives like alibaba_get_product_details or aliexpress_search_products. Context is only implied; there are no prerequisites, exclusions, or when-not-to-use statements.

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 lists the cost components (fees, shipping, ads, refunds) and ROI, but does not disclose output format, assumptions, or limitations. The description provides basic behavioral transparency but lacks depth.

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 consists of two short sentences with no wasted words. It is front-loaded with the core purpose and adds a marketing line that, while not essential, does not detract. Excellent 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 the lack of an output schema, the description should explain what the tool returns. It does not mention the output format, how the calculation is performed, or any defaults. For a comprehensive calculator with 8 parameters, this is a significant gap.

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 meaning by mentioning the overall scope (Amazon fees, etc.) but does not elaborate on individual parameters beyond what the schema already provides. Thus, it meets but does not exceed the 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 tool calculates full P&L including Amazon fees, shipping, ads, refunds, and ROI, distinguishing it from sibling tools that are for product search or details. The verb 'Calculate' and resource 'P&L' are specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for profit calculation but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. Given the clear purpose, it scores at the implied usage level.

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 bears full responsibility for behavioral disclosure. It only lists return fields but does not mention pagination behavior, result ordering, rate limits, or any side effects. This is insufficient for a tool with 7 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 clear sentence that immediately states the tool's purpose and key outputs. No unnecessary words.

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 7 parameters and no output schema or annotations, the description is adequate but minimal. It does not explain how to use filters effectively or describe the response structure, which would be helpful 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?

The input schema has 100% description coverage, so the parameters are already well-documented. The description adds no additional semantic information about the parameters beyond what is in the schema.

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

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

The description clearly states the tool searches AliExpress for products and lists the returned fields (titles, prices, ratings, orders count, seller info). It distinguishes from Alibaba tools by specifying 'AliExpress'. However, it does not explicitly differentiate from the sibling tool 'aliexpress_get_product_details' which is for single product details.

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 the many sibling search tools (e.g., 'alibaba_search_products', 'aliexpress_analyze_supplier') or when not to use it. The description lacks any context about prerequisites, sorting, 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?

With no annotations provided, the description carries the full burden. It implies a read-only analysis by stating it analyzes and returns data, but does not explicitly declare non-destructiveness or discuss authentication, rate limits, or side effects. The description is adequate but lacks depth.

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 a list of returned items, which is concise and front-loaded. However, the list is embedded in prose rather than structured (e.g., bullet points), slight room for improvement.

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 one parameter, no output schema, and no annotations. The description lists return fields but omits data types, result format, or whether estimates are real-time. It is minimally complete but could provide more detail for an agent to fully understand the output.

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 a single parameter 'keyword' described as 'Keyword to analyze competition for'. The tool description merely says 'a keyword', adding no new meaning beyond the schema. 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 verb (Analyze), resource (competition level for a keyword), and lists specific return metrics (seller count, average price, review counts, market saturation). It distinguishes itself from siblings like 'amazon_analyze_listing_quality' by focusing on competition analysis.

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 such as 'amazon_keyword_suggestions' or 'amazon_search_products'. There is no mention of prerequisites or contexts where this tool is preferred.

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 provided, so the description carries full burden. It mentions scoring and checking elements but lacks details on behavior (e.g., error handling for invalid ASIN, read-only nature, or output format). The description is minimal for a tool with no annotations.

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

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

The description is two sentences, front-loading the core function and listing checked elements. No extraneous information.

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

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

No output schema exists, yet the description does not explain what the tool returns (e.g., a single score or breakdown). Given the tool's complexity (scoring multiple aspects), the description is 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 coverage is 100% with only one parameter (asin) described. The description adds no additional meaning beyond 'Amazon ASIN', so it meets baseline but does not enhance parameter understanding.

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 scores listing quality (0-100) for CTR and conversion, and lists specific elements checked (title, bullets, images, A+ content). This distinguishes it from sibling tools like amazon_analyze_competition or amazon_analyze_negative_reviews.

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 for evaluating listing quality but does not explicitly state when to use this tool versus alternatives. With many Amazon siblings, guidance on when this tool is preferred (e.g., for optimization) would be beneficial.

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 implies a read-only analysis by stating 'analyze' and 'extracts', but does not explicitly confirm non-destructive behavior or disclose any side effects. Adequate but not explicit.

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

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

Two concise, front-loaded sentences that together convey purpose and output without wasted words. Every sentence 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?

With no output schema, the description informs the agent of the output format (common complaints, defect patterns, improvement opportunities). For a two-parameter tool, this is largely sufficient, though a mention of the return structure could enhance 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?

Schema coverage is 100%, so baseline is 3. The description adds minimal parameter-level meaning beyond the schema—it confirms ASIN identifies the product and that max_reviews controls review count. No further elaboration.

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 analyzes negative reviews for a product and extracts common complaints, defect patterns, and improvement opportunities. This distinguishes it from sibling tools like amazon_analyze_competition and amazon_analyze_listing_quality.

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 does not provide any guidance on when to use this tool versus alternative analysis tools. No exclusion criteria or alternative tool names are mentioned, leaving the agent without context for 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?

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions output data types but omits critical traits: it does not state that the tool is read-only, requires authentication, has rate limits, or what happens on error. For a tool with zero annotations, this is insufficient.

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 of 12 words that efficiently conveys purpose and output. No unnecessary words or repetition.

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 low complexity (2 optional parameters, no output schema), so the description covers the core function. However, without an output schema, some description of return structure or pagination would enhance completeness. It is minimally adequate.

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% (both 'limit' and 'category' have descriptions). The tool description adds 'by category' which aligns with the category parameter but provides no additional semantic value beyond the schema. Baseline 3 is appropriate.

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

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

The description uses a specific verb ('Get') and resource ('best-selling products by category'), clearly distinguishing it from siblings like amazon_search_products (general search) or amazon_bsr_history (historical BSR). It also lists the types of data returned (sales estimates, BSR, pricing, trend), making the tool's function unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly suggests use for browsing category best sellers, but provides no explicit guidance on when to use this tool versus siblings like amazon_search_products or amazon_estimate_sales. No when-not-to-use or alternatives 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?

No annotations provided; description mentions Keepa data but fails to disclose behavioral traits such as data freshness, error handling, or whether the tool requires permissions. For a read tool, this is insufficient.

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

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

Two sentences, no redundancy, front-loaded with action and resource. Every sentence provides value.

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?

Tool is simple with 2 params and no output schema. Description covers purpose and data source but lacks information on return format or pagination, making it only adequately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers both parameters with descriptions; description adds 'Keepa data' as external context but no param-specific enrichment beyond schema. Baseline 3 is appropriate given 100% 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?

Description clearly states verb 'Get' and resource 'BSR history' using Keepa data, distinguishing it from sibling tools like amazon_best_sellers (current list) and amazon_price_history (price data).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like amazon_best_sellers or amazon_estimate_sales. The description implies tracking trends but does not specify use cases 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?

Without annotations, the description carries the full burden. It discloses the tool estimates and uses BSR, reviews, and market data, but does not mention return format, accuracy, or limitations (e.g., it's an estimate, not actual sales data). No contradiction with annotations.

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?

Single sentence, no waste. Could be improved by mentioning the input parameter explicitly, but overall concise and 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?

For a tool with no output schema and simple input, the description lacks detail on what the agent can expect as output (e.g., numbers, ranges, units). Given the context of many sibling tools, more completeness would aid selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% with one parameter 'asin'. Description adds context that the tool uses BSR, reviews, and market data internally, but does not clarify that the only input is ASIN. Thus it provides marginal value beyond the schema.

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

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

The description clearly states the tool estimates monthly sales and revenue based on BSR, reviews, and market data. It distinguishes from siblings like amazon_best_sellers (lists top sellers) and amazon_bsr_history (historical BSR) by focusing on sales estimation. However, it does not explicitly differentiate from amazon_opportunity_score or other estimation tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. For example, when should an agent choose this over amazon_opportunity_score or amazon_price_history? The description only states what it does, not when it's appropriate or what prerequisites exist.

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 full burden but only lists returned fields. It fails to disclose behavioral traits like rate limits, authentication needs, error handling, or side effects. The read-only nature is inferred, not 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 a single, information-dense sentence. It is concise and front-loaded with the core action, though it could be slightly more structured for readability. No wasted words.

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 get-details tool with one parameter and no output schema, the description provides a good list of returned fields. It lacks error handling or pagination info, but these are less critical for this straightforward 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 coverage is 100% with a clear parameter description. The description adds value by listing the data returned (e.g., Keepa data), which aids interpretation beyond the schema. However, it doesn't elaborate on parameter constraints beyond the example.

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 gets detailed product info by ASIN and lists specific data fields. It distinguishes itself from sibling search tools like amazon_search_products and analytical tools like amazon_analyze_competition, making its purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention exclusions or context, such as preferring amazon_price_history for historical price data. Usage is only implied by the tool's name and description.

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?

The description discloses that it returns keywords with estimated search volume and competition, which is adequate for a read-only tool. However, it does not mention any potential limitations, rate limits, or error handling, though none are required given the simplicity.

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, using two short sentences that convey the core purpose and output without any unnecessary words.

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 tool with one parameter and no output schema, the description is reasonably complete. However, it could mention the number of suggestions returned or the scale of competition to improve clarity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides 100% coverage for the single parameter 'seed_keyword' with a clear description. The tool description does not add extra semantic detail beyond the schema, but the overall context (returns volume and competition) is helpful.

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 keyword suggestions' and the resource 'Amazon'. While it distinguishes from some siblings by focusing on keyword suggestions rather than product search, it does not explicitly differentiate from other suggestion-related tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like amazon_search_products or amazon_analyze_competition. The description lacks context for appropriate usage.

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 cover behavioral traits. It describes a read-like calculation with no side effects, but does not explicitly state it is read-only or disclose any limitations. The description is adequate but not detailed.

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 two short sentences that immediately state the purpose, range, and analysis factors. No unnecessary information.

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

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

Given the low complexity (1 parameter, no output schema, no annotations), the description is fairly complete. It explains the input, the score range, and factors. It could be improved by mentioning the output format, but it is adequate for a simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with a clear description for the 'asin' parameter. The tool description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.

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

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

The description clearly states the tool calculates an opportunity score for a product niche, specifying the range (0-100) and key factors (demand, competition, margins, market trends). This distinguishes it from sibling tools like amazon_analyze_competition or amazon_get_product_details.

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 what the tool does, but does not explicitly state when to use it versus alternatives or when not to use it. The distinction from siblings is implied by the unique scoring function.

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 states the outputs ('price over time, deal detection, and price statistics') but does not disclose whether the operation is read-only, requires authentication, or has rate limits. The behavioral traits are partially transparent but insufficient for a fully informed selection.

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

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

The description is a single sentence that efficiently conveys the core purpose and outputs. It is front-loaded and wastes no words. However, it could benefit from a slightly more structured format (e.g., bullet points) without adding length.

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 output schema and no annotations, the description covers the essential functionality but leaves gaps. It explains what the tool does and what it returns, but lacks details on data freshness, time zones, or pagination. It is adequate but not comprehensive for a data-retrieval 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 covers both parameters with descriptions (100% coverage). The description adds no additional meaning beyond labeling the overall function. For example, it does not elaborate on the 'days' parameter's role in limiting history or that 'asin' is a standard Amazon identifier. Baseline 3 is appropriate.

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

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

The description clearly states the tool retrieves 'price history for a product' and lists specific outputs ('price over time, deal detection, and price statistics'). It uses a specific verb-resource combination and distinguishes it from sibling tools like amazon_bsr_history and amazon_get_product_details.

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 mentions 'using Keepa data' but provides no explicit guidance on when to use this tool versus alternatives like amazon_analyze_competition or amazon_estimate_sales. It lacks when-to-use, when-not-to-use, or prerequisite conditions.

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 provided; description only states it searches and returns fields. No disclosure of behavior like no results handling, authentication, or rate limits.

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

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

Two short sentences, no wordiness, directly to the point.

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 9 parameters, no output schema, and many sibling tools, the description is minimal. Lacks details on pagination, sorting, or effect of filters. Incomplete for a complex search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds value by listing return fields (titles, prices, etc.) that are not in the schema, though it doesn't elaborate on parameter usage beyond schema.

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

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

Description clearly states verb 'Search' and resource 'Amazon products', specifies return fields (titles, prices, etc.). Distinct from sibling tools like amazon_best_sellers or alibaba_search_products.

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 versus other Amazon tools (e.g., amazon_best_sellers, amazon_analyze_competition). No exclusions or alternative mentions.

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