LemmeBuyIt

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

Annotations already declare readOnlyHint and destructiveHint, so description adds value by detailing included data points, ascending sort, and omission of weeks with no samples. No contradiction. Could mention rate limits but not necessary.

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?

Concise, front-loaded with purpose, then details. Each sentence serves a purpose; 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 no output schema, description fully explains output fields (price, rank, counts, etc.) and sorting. Completeness for a read-only tool with clear input and 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 description coverage is 100%, so baseline 3. Description adds context about ASIN and weeks but does not provide new parameter-level details 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?

Clearly states it gets chart-ready weekly Amazon catalog history for a specific ASIN. Distinguishes from sibling 'get_product_price_history' by specifying Amazon scope.

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?

Explicitly says 'Use this when the user provides an ASIN directly' and contrasts with 'get_product_price_history' for Walmart-scoped history, providing clear when-to-use and alternative.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds rich behavioral context: returns time series sorted ascending by ts, omits weeks with no samples, specifies included fields (Walmart price, Amazon ASIN data if UPC maps, BSR, etc.). No contradictions.

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: first sentence lists return structure and sorting, second sentence provides usage guidance. No unnecessary words, front-loaded with purpose.

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

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

Given no output schema, the description fully explains the return format (time series, fields, sort order, omission of empty weeks). It also guides on alternatives. No critical gaps like pagination or rate limits, but the read-only nature and week cap make it sufficient.

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

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

Schema coverage is 100% with descriptions for all 3 parameters. The description adds value by clarifying that product_id corresponds to 'unique_merchant_sku' from search results, weeks defaults to 52, and retailer_id comes from list_retailers. This exceeds the baseline of 3.

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 chart-ready weekly price and catalog history for a product, with specific verb 'get' and resource 'product price history'. It distinguishes from sibling tools like get_retailer_product by noting it returns time-series data versus rolled-up averages.

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?

Explicitly says 'Use this when a user wants to visualize how a product's price or catalog signals changed over time.' and provides a direct alternative: 'For just the rolled-up price averages... use get_retailer_product and read price_averages instead.'

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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that it lists categories for a specific retailer, confirming read-only nature. Does not add further behavioral details like pagination or output format.

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

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

Two efficient sentences, no redundancy. Front-loaded with primary action and purpose.

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

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

Given tool simplicity (1 param, no output schema), the description adequately defines its role and usage context. Slight lack of output format hint, but not critical.

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 well-described retailer_id param. The description does not add additional param meaning beyond the schema, 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 'List product categories available for a specific retailer' with a specific verb-resource combination. It distinguishes from sibling tools like search_retailer_products and list_retailers by focusing on categories per retailer.

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?

Explicitly says 'Use this to discover valid category paths before filtering products by category.' Provides clear context for when to use, though lacks explicit when-not-to-use or alternatives beyond the param hint to list_retailers.

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?

Annotations already provide readOnlyHint=true and destructiveHint=false. The description adds context about what data is returned (pricing, enrichment, etc.) but does not disclose additional behavioral traits like authentication needs 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?

The description is two sentences long, front-loads the core purpose, and provides actionable context without any 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 (2 required params, no output schema, annotations present), the description covers the purpose, input origin, and return content. It lacks explicit pagination or format details but is adequate for a 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 coverage is 100% with clear descriptions for both parameters (product_id as unique SKU, retailer_id with example and reference to list_retailers). The description reinforces the usage context (after search) but does not add significant meaning 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 verb ('Get'), the resource ('full details for a specific product'), the identifier ('retailer SKU/ID'), and the scope of data ('pricing, Amazon enrichment, profitability, and historical Amazon catalog metrics'). It also distinguishes from search tool by targeting a single product.

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 explicitly says 'Use this after search' to retrieve complete product data. This provides clear context and sequence relative to sibling tools like search_retailer_products, though it doesn't list when to avoid using 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?

Annotations already provide readOnlyHint=true and destructiveHint=false, signaling a safe read operation. The description adds the context of checking health status, but does not disclose further behavioral traits such as caching, rate limits, or response 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, front-loaded sentence that conveys the tool's purpose with no wasted words. Every part of the sentence is essential.

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 no parameters, no output schema, and annotations cover safety, the description is mostly complete. It could benefit from mentioning the response format (e.g., returns OK or detailed status), but the current text is sufficient for a simple health check.

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 zero parameters, schema coverage is 100% by default. The description adds no parameter details as none exist, which meets the baseline of 4 for tools with no parameters.

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 uses the verb 'check' and identifies the resource as 'health and operational status of the LemmeBuyIt API service', making the purpose unmistakable. It distinguishes itself from sibling tools, which focus on prices, retailers, and 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 does not explicitly state when to use this tool versus alternatives. However, its unique purpose as a health check implicitly guides usage, and no sibling tools overlap in 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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds the key behavioral constraint that results are scoped to the API key, which is not in annotations. This provides useful context beyond what structured fields offer.

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 with zero waste. First sentence states purpose, second provides critical usage guidance. 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 no parameters, no output schema, and a simple purpose, the description is fully complete. It covers what the tool does, when to use it, and the scoping constraint.

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

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

The tool has 0 parameters and schema coverage is 100% (empty schema). No parameter info is needed. The baseline for no parameters is 4, and the description adds no redundant parameter details.

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 'List all retailers available to your API key', which is a specific verb+resource. It also distinguishes from sibling tools by advising to call this first to discover retailer IDs before searching products, directly contrasting with search-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?

Explicitly instructs 'Always call this first to discover valid retailer IDs before searching products', providing clear when-to-use and implied when-not-to-use (not for product searches). This guides the agent to use it as an initial discovery step.

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?

Annotations declare readOnlyHint=true (safe read) and destructiveHint=false. The description adds substantial behavioral details: auto-detection of search_query (numeric→GTIN, text→full-text), batch lookup support, pagination mechanics, and limit recommendations for large catalogs. No contradictions 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?

The description is concise (3 sentences) and front-loaded with the main purpose. Every sentence adds value: overview of capabilities, pagination instruction, and large-catalog guidance. No redundancy or 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?

Given 40 parameters and no output schema, the description covers key behaviors (pagination, auto-detection, batch, category path format, limit suggestions). It references sibling tools for category discovery. However, it does not describe the response structure or fields, which could be helpful since no output schema is provided.

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 detailed schema descriptions for each parameter. The tool description adds aggregate context (e.g., pagination with 'after' and 'next_cursor', batch lookups, limit range suggestions) that enhances understanding of parameter interactions beyond individual schema entries.

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

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

The description clearly states the tool's purpose: searching and filtering products from a specific retailer's catalog. It lists supported features (text search, barcode lookups, price/discount filtering, pagination) and distinguishes from sibling tools like get_retailer_product (single product) and get_retailer_categories (categories).

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 good usage context, including pagination with 'after' cursor, batch lookups via comma-separated values, and auto-detection of barcode vs text. It references get_retailer_categories for category paths. However, it does not explicitly state when not to use this tool or compare it to alternatives like get_retailer_product.

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