store

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

Beyond the read-only and idempotent annotations, it discloses the 120 DPI contract rule, per-size required_px output, country-dependent behavior, and the fallback behavior when dimensions are omitted. This is rich behavioral context.

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

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

Four sentences with each one adding a distinct piece of information: core purpose, return type, scope boundary, country behavior, and omission caveat. No filler.

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

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

The description is complete for a read-only, idempotent tool with an output schema and fully documented parameters. It covers the central rule, optional behavior, exclusions, and edge case.

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 the baseline is 3. The description adds semantics beyond the schema by explaining that omission of width/height is allowed but results in an unverifiable report, and that country enables market-visible wall formats.

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 gives a specific verb ('Check') and a concrete resource: whether pixel dimensions meet the 120 DPI hard gate used by upload. It also differentiates itself by clarifying that crop and artefact review happen in the configurator, not here.

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 implies when this tool is relevant for upload compatibility checking and points to the configurator for crop/artefact concerns. It does not explicitly name a sibling alternative, but the context makes the boundary clear.

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 must carry the full behavioral burden. It usefully discloses the 120 DPI hard gate and the behavior when width/height are omitted: suitability cannot be verified. However, it leaves important behavior undisclosed, such as whether this is an asynchronous background task, whether it has side effects, and what the report returns.

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, dense sentences with no fluff. The core rule is front-loaded, and the omit-instruction is concise but meaningful. 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?

The tool has no annotations and no output schema, so the description must compensate. It does not explain what the returned report contains, how the task is executed, or how it relates to 'check_photo_compatibility'. For a task-named tool with up to four parameters, these are notable gaps.

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 value beyond the schema by explaining that width/height should be omitted to signal that verification is impossible without dimensions, giving the agent actionable parameter-level knowledge not available from the schema alone.

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 names the resource (photo compatibility) and the specific action (resolution check) against a concrete, meaningful rule: the print-contract 120 DPI hard gate used by upload. It does not, however, explicitly differentiate this from the sibling tool 'check_photo_compatibility', other than the 'Task-based' prefix.

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 no guidance on when to use this task variant versus the non-task sibling 'check_photo_compatibility', nor does it state when this tool is preferred. 'Used by upload' provides some context, and the omit-width/height instruction explains input behavior, but not tool selection.

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

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

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 read-only, open-world, and idempotent hints, but the description adds significant behavioral detail: default compact card shape, optional detailed payload contents, and a response-size cap that applies to both modes. It also distinguishes the compact vs. detail behavior precisely. 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 front-loaded with the core purpose and then organizes behavior by default and detail modes. Every clause adds information—return fields, mode conditions, and the response cap—without filler. Despite its density, the structure makes it easy to scan.

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 six-parameter search tool, the description covers all invocation modes, return shapes, and important constraints like the server response-size limit. With annotations and an output schema also available, nothing essential is missing for an agent to correctly call the tool.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds interaction semantics beyond the schema by clarifying that `detail` is only meaningful with `handle`, that default false returns the compact card shape, and by enumerating exactly which fields each mode returns. This goes beyond repeating schema 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 states a clear verb and resource: search and browse Bolot Studio custom metal photo prints, and it details both the default compact card and the optional enriched detail response. This makes the tool's role unambiguous. It does not explicitly contrast itself with sibling search_products_task, so it stops short of full sibling differentiation.

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 practical usage guidance for parameter combinations: pass `handle` for a single product, add `detail: true` for the enriched record, and default to compact cards for all searches. It clearly explains the conditions under which `detail` matters. It does not say when to prefer alternatives like compare_products or search_products_task, so there are no exclusions.

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

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

With no annotations, the description carries the full behavioral disclosure burden. It does disclose the task/polling model and long-running nature, which is useful, but it omits details about task lifecycle, failure modes, rate limits, or whether this is a read-only operation.

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 with no filler. It front-loads the core behavior and immediately tells the agent how to retrieve the result, making it appropriately sized and easy to parse.

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

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

The async task/polling mechanic is mentioned, and the schema fully documents parameters, so the tool is callable. However, without an output schema or an explicit alternative/selection policy, the description leaves some context about result shape and when to choose the synchronous counterpart to inference.

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%, and each parameter already carries rich descriptions with examples. The tool description itself adds no parameter-level guidance, so the baseline score of 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?

The description states a clear action ('product search') with a specific resource ('products') and a distinguishing retrieval mode ('task-based... task polling'). It separates the tool from the synchronous-looking sibling search_products, although it does not explicitly name that tool.

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 actionable context: this is intended for long-running MCP clients and results are retrieved by polling. It clearly implies an asynchronous pattern, but it does not explicitly state when to prefer this over the sibling search_products tool.

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.