store

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds context beyond annotations by noting it's non-diagnostic, conditional on country, and directs customers to the configurator flow. 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?

The description is a single dense sentence of about 30 words, front-loaded with the main purpose. Every clause adds value: the caution, the conditional behavior, and the redirect. No fluff or redundancy.

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

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

Given the rich schema coverage, output schema, and annotations, the description covers the key behavioral aspects: non-diagnostic nature, conditional on country, return of market-visible formats, and configurator flow. It lacks explicit mention of when to use this over the sibling task tool, but is otherwise complete for a read-only 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%, so the schema already documents all four parameters. The description reinforces the importance of 'country' and cautions about dimensions, but adds little new semantic detail beyond what the schema provides. 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 it 'Start[s] a non-diagnostic photo-suitability review,' which is a specific verb+resource combination. It distinguishes itself by mentioning market-visible wall formats and directing to the configurator flow, but it doesn't explicitly differentiate from the sibling check_photo_compatibility_task.

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?

Provides clear context: 'with an explicit delivery country, returns only market-visible wall formats' indicates when to expect certain output. The caution that 'Pixel dimensions alone never prove quality or compatibility' implies this tool is needed for a proper review. However, it doesn't state explicit alternatives or when-not-to-use cases.

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 discloses that the tool is task-based and long-running, and that polling is required. However, it does not describe important behavioral aspects such as failure modes, rate limits, or the nature of the result object. This leaves significant gaps for safe usage.

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 concise sentences, but the first sentence is dense with jargon ('non-diagnostic', 'photo-suitability review'). While compact, clarity could be improved for new users. No structural issues, but could be more accessible.

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 description mentions task-based execution and polling, which are essential for usage. However, it does not explain the output format, possible results, or error handling. Given the lack of an output schema, more detail on the pollable response would improve 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 description coverage is 100%, so the schema already provides clear definitions for all four parameters. The tool description adds no additional parameter-level meaning beyond what the schema offers. Per guidelines, 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 identifies the tool as a task-based photo compatibility check, specifically for long-running MCP clients. It distinguishes from the sibling 'check_photo_compatibility' by indicating this is a task/polling variant. The phrase 'non-diagnostic' is slightly ambiguous but overall purpose is clear.

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 advises using task polling to retrieve results and notes that pixel dimensions alone are insufficient. However, it does not explicitly state when to choose this tool over the synchronous alternative or provide exclusion criteria. Usage guidance is implied but not fully 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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the agent knows this is a safe read-only operation. The description adds the specific comparison categories but does not disclose any additional behavioral traits such as output format, data sources, or potential limitations. Given the annotation coverage, this is acceptable but not especially rich.

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 communicates the tool's purpose and key comparison modes without unnecessary words or repetition. It is front-loaded with the main action and resource, making it easy for an agent to quickly grasp.

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 four parameters (one required), an output schema, and read-only annotations, the description covers the essential context: what is being compared and the two comparison modes. It does not need to explain return values since an output schema exists. A minor gap is the lack of explicit guidance on when to prefer this over sibling tools, but the overall context is sufficient for effective use.

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 with descriptions for all four parameters, including the enum for comparison_type. The description adds some context by naming examples of the comparison types, but it does not further clarify locale, country, or product_handle beyond what the schema already states. Baseline 3 is appropriate when schema fully documents 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 states the tool compares Bolot Studio products with specific comparison types (Legacy vs Cinematic, or vs competitors like Displate, Canvas, Framed prints). The verb 'compare' plus the listed resources and comparison modes make the purpose unmistakable and distinguish it from sibling tools like get_product_recommendations.

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 types of comparisons are supported (internal vs competitor), which implies when to use the tool. However, it does not explicitly state exclusions or alternatives, such as using get_product_recommendations for product suggestions rather than direct comparisons. Still, the purpose is specific enough that an agent can infer 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?

Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety profile. The description adds minimal extra behavioral context beyond these annotations, merely restating the input criteria. It does not disclose additional traits such as recommendation count, sorting, or response structure, but the output schema likely covers return format. 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?

The description is a single, concise sentence that front-loads the verb 'Get' and the resource, then enumerates key input dimensions. Every phrase earns its place; no filler 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 a rich input schema (7 params, all documented) and an output schema, so the description does not need to explain return values. The description provides a clear summary of the tool's purpose and key inputs. It could add more context about when it's preferable over search_products, but given the schema richness, the description is sufficiently 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 coverage is 100% with all parameters described. The description lists several parameters (intent, budget, recipient profile, room type, lighting) but adds no additional semantic meaning beyond what the schema already provides. It omits locale and country, but the schema fully documents these, so the description does not need to compensate.

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

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

The description clearly states the tool's function: 'Get personalized product and variant recommendations' with specific input criteria (intent, budget, recipient profile, room type, lighting). This specific verb+resource combination distinguishes it from sibling tools like search_products, which likely performs keyword-based search rather than context-driven recommendations.

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 personalized recommendations are needed based on customer intent and other criteria, but it does not explicitly state when to use this tool versus alternatives (e.g., search_products). There is no mention of exclusions or alternative tools, so the guidance is only implicit.

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, openWorldHint, and idempotentHint, covering safety and idempotency. The description adds useful return semantics ('Returns ratings, distribution, and highlighted reviews') and the notion of 'featured' excerpts, which go beyond the schema. No contradictions found.

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?

Three concise sentences with no filler. The first sentence states the action, the second gives usage guidance, and the third lists return components. Every sentence contributes value and the structure is front-loaded.

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

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

This tool has a single optional parameter, a rich output schema, and strong annotations. The description adds the practical use case and return summary, making it complete for selection and invocation. No further explanation of return values is needed given the output schema.

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% and the parameter description is detailed ('Product handle: ... Omit for store-wide stats.') The tool description itself does not add parameter-related meaning, so the baseline of 3 is appropriate.

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

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

The description opens with a specific verb and resource: 'Get aggregated customer review statistics and featured review excerpts.' It clearly distinguishes this tool from siblings like search_products or compare_products by focusing on review aggregation and social proof.

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?

It provides explicit when-to-use context: 'Use this when a customer asks about product quality, other customers' experiences, or social proof.' However, it does not explicitly mention when not to use it or name alternative tools, stopping short of a 5.

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 and idempotentHint, so the safety profile is covered. The description adds output details (delivery time, threshold, carrier) but does not disclose edge cases or error behavior. Adequate given annotation coverage.

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, front-loaded with action and outputs, then usage guidance. No filler 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?

For a simple read-only tool with one parameter, an output schema, and strong annotations, the description provides essential purpose, scope, and usage context. It is complete for an agent to select and invoke the tool correctly.

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 covers country_code with ISO 3166-1 alpha-2/alpha-3 examples, achieving 100% coverage. Description adds no additional parameter meaning beyond reinforcing the country scope, so baseline 3 applies.

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

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

Description states a specific verb 'Get' with three concrete outputs (delivery time, free shipping threshold, carrier information) scoped to a specific country, clearly distinguishing it from sibling tools like product search or store info.

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?

Includes explicit usage instruction 'Use this when a customer asks about shipping costs or delivery times to their location,' providing clear context. It doesn't mention alternatives or exclusions, but no direct sibling alternative exists.

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 and idempotentHint, so the description does not need to restate safety. It adds value by enumerating the exact content categories returned (brand info, shipping zones, payment methods, configurator URLs), which goes beyond the tool name. 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?

The description is two concise sentences, front-loaded with the key value and free of fluff. 'Essential context for helping customers' is brief and meaningful, though slightly vague.

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 output schema exists and annotations cover safety, the description sufficiently lists the data categories covered. It is complete enough for an agent to know what to expect, though it could slightly benefit from noting locale/country influence on returned data (already captured in schema).

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 full descriptions for both parameters (locale and country), including examples and semantics. The description does not add parameter-level detail, but the schema's 100% coverage makes this acceptable 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 retrieves Bolot Studio brand information, shipping zones, payment methods, and configurator URLs, using a specific verb ('Get') and resource scope. This distinguishes it from sibling tools like search_policies_and_faqs or get_product_recommendations, which focus on other domains.

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 a general context—'Essential context for helping customers'—implying when to use the tool, but it does not explicitly state when to use it over alternatives or any exclusion criteria. Sibling tools are not named or contrasted.

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, openWorldHint=false, and idempotentHint=true, so the safety profile is clear. The description adds no extra behavioral context like pagination, authentication, or locale-specific behavior, but it does not contradict 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 a single, front-loaded sentence that directly states purpose and key coverage topics. No filler words or redundant details.

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 of a search tool, the presence of an output schema, and strong annotations, the description sufficiently captures the tool's purpose and scope. It clearly defines what is searched and which topics are covered, with no missing critical context.

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

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

Schema description coverage is 100%, with each parameter (query, locale, country) already described with examples. The description does not add additional parameter semantics beyond the schema's own descriptions.

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 Bolot Studio FAQs and store policies, with a specific verb and resource. It also lists coverage areas (product questions, sizing, finishes, shipping, returns, ordering) that distinguish it from sibling product search tools like 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 for policy/FAQ questions via the coverage list, but it does not explicitly say when to use this tool instead of alternatives such as search_products or get_store_info. No exclusions or alternative recommendations are provided.

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

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds value beyond these by disclosing the response shape (compact vs detailed), the condition for getting the enriched record, and a notable trait: every response is capped to the server's response size limit. 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 one long, information-dense sentence, but it is front-loaded with purpose and return-shape. Every clause contributes necessary details (what the card contains, when to use detail, response cap). It could be split into shorter sentences for readability, but there is no wasted 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 presence of an output schema, annotations, and 5 well-documented parameters, the description covers the essential operational semantics: default vs detail mode, parameter interplay, and response size limitation. It does not explain error cases or rate limits, but those are not strictly required for a read-only search tool with this level of schema support.

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 goes further by explicitly linking the 'handle' and 'detail' parameters, explaining what each return mode includes (compact card fields vs full enriched record), and noting that 'detail' is only effective when 'handle' is provided. This adds relationship semantics not fully captured 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 starts with a specific verb+resource combination: 'Search and browse Bolot Studio custom metal photo prints.' It clearly distinguishes itself from sibling tools like compare_products and get_product_recommendations by being the general search/browse endpoint. The return shape is also described precisely (compact product card), which anchors the tool's purpose.

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 gives clear context on when to use the tool and its modes: search/browse for all products, and single-product lookup with 'detail: true' when you pass a handle. It does not explicitly name alternatives or exclusions (e.g., 'for comparisons use compare_products'), but the instructions for switching modes are unambiguous and sufficient.

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 the key asynchronous behavior: results must be retrieved via polling. Since no annotations are provided, the description carries the full burden, but it does not mention read-only nature, error handling, or how to start the task beyond the polling hint. It adds some context but leaves gaps.

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

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

The description is only two sentences, front-loading the purpose and the critical polling instruction. Every word contributes value with no fluff, making it highly concise and well-structured.

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

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

The tool's asynchronous nature and lack of output schema mean the description should clarify the task/result flow. It points to polling, but does not explain what the task response looks like, how to poll, or any timeouts. The schema is rich, but the description leaves some important context out, making it adequate but not fully 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?

The input schema covers 100% of parameters with descriptive text for each (query, detail, handle, locale, country). The tool description itself adds no parameter-level meaning, but the schema already handles parameter semantics thoroughly, so a 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 it is a product search tool using a task-based mechanism for long-running MCP clients. It uses a specific verb ('search') and resource ('products'), and differentiates itself from the sibling 'search_products' by emphasizing the asynchronous, polling-based nature.

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?

It gives guidance on when to use this tool: for long-running MCP clients. It also instructs to use task polling to retrieve the result. However, it does not explicitly name alternatives like 'search_products' or state when not to use this tool, though the sibling context implies 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 declare readOnlyHint and idempotentHint, so the description doesn't need to repeat safety traits. It adds valuable behavioral context beyond the schema, such as the address sign ignoring size/finish and specific physical dimensions for each size token.

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, dense paragraph that front-loads the purpose and then provides product-specific rules. Every clause adds value, though it is slightly verbose due to the detailed size specifications. It is appropriately structured for the complexity.

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 description, combined with the full schema descriptions and annotations, thoroughly covers the tool's behavior. It specifies valid size/finish combinations, all product handles, and the address sign exception. Given the output schema exists, return values needn't be described.

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 each property described, but the description adds meaningful detail beyond the schema, including physical dimensions (13×9 cm panels, 30×20, 42×30) and the address sign's template behavior. This enriches the 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 tool generates a direct URL to the product configurator with pre-selected options, using a specific verb 'Generate' and a clear resource. It distinguishes itself from sibling tools that search, compare, or fetch product info.

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?

It provides detailed context on valid sizes per product and explicitly notes the address sign ignores size/finish, guiding correct usage. However, it does not explicitly name alternative tools or state when not to use this tool, so it lacks explicit exclusion criteria.

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