glowjakt

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

Beyond the annotations (readOnlyHint, destructiveHint), the description reveals rich behavioral details: it includes sold-out stores, explains that store_count matches search_beauty_prices, and provides nuanced distinctions like is_cheapest vs. cheapest_store_incl_shipping. It also discloses the computation of landed costs from shipping rules, which is not implied by the schema or 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 detailed but every sentence adds value. It opens with the core purpose, then covers return structure and key behaviors, and ends with parameter explanations. No redundancy or fluff; appropriate length for the tool's 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?

Given the output schema exists and the description explains what is returned (price, stock status, links, landed cost fields), the description is complete. It also references related tools, covers edge cases (sold-out offers), and provides operational details (shipping rule calculations). The tool is self-contained and clear.

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?

Since the schema provides no parameter descriptions (0% coverage), the description fully compensates. It explains that product_slug comes from search_beauty_prices and that country lists specific markets ('se', 'no', 'dk', 'fi') with a hint to check get_service_info for updates. This adds meaning beyond the bare 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's function: 'Compare prices for one product across all stores that sell it.' It also distinguishes itself from siblings by referencing search_beauty_prices for product_slug and explaining how store_count matches, differentiating the compare operation from 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?

Explicit guidance is given: the description instructs to obtain product_slug from search_beauty_prices, notes that get_service_info returns the current list of markets, and advises preferring the landed-cost answer when the user asks 'where should I buy this?'. This clarifies when and how to use the tool relative to 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?

Annotations already declare readOnlyHint and destructiveHint, but the description adds non-obvious context: discount basis against store's own price, exclusion of inflated-list stores, shipping exclusion, and the nuanced behavior of min_discount on returned counts vs total_on_sale. These are valuable behavioral insights beyond 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 well-structured with a clear opening purpose, then contextual notes, and per-argument bullet points. Every sentence adds value; no fluff. It front-loads the core function and keeps related notes concise.

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

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

Given an output schema exists (not required to explain return format), the description covers all input semantics, ties to related tools for verification, and explains edge cases (one-off clearance, shipping exclusion). It is complete for a tool with four optional parameters and clear alternatives.

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 has 0% description coverage, but the description fully compensates by explaining each parameter: country with market list and reference to get_service_info, category with valid values, min_discount with effect on counts, and limit with range. Adds meaning well beyond the raw 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 it finds beauty products on sale, sorted by discount, and differentiates from siblings by explicating the discount computation against store's own list price rather than Glowjakt's history, plus mentions exclusions. This is a specific verb+resource with clear 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?

Provides explicit guidance: when matters, verify with get_price_history; compare_product_prices for landed cost; get_service_info for current market list. It also notes that stores with fake discounts are already excluded, implying this tool is the default for browsing deals. Clear when-to-use and when-not-to-use.

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 mark the tool readOnly and non-destructive. The description adds behavioral context: data is the lowest price per day, history is recent, and the country market list may expand over time, with get_service_info as the source of truth.

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 compact and front-loaded: a clear one-sentence purpose, example questions, then parameter details. 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?

With two simple parameters, an output schema available, and a clear use-case statement, the description provides enough context for an agent to select and call the tool. The links to sibling tools fill in remaining integration 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?

Despite no schema-level parameter descriptions, the description's Args section fully explains product_slug as coming from search_beauty_prices and country as a market with current valid values ('se', 'no', 'dk', 'fi') and the get_service_info fallback. This adds substantial meaning beyond the input 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 states 'Get the recent price history (lowest price per day) for a product' with a specific verb and scope. It is clearly distinct from siblings like search_beauty_prices or compare_product_prices because it focuses on time-series price history.

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 concrete use cases ('is this a good deal right now?', 'has the price dropped recently?') and directs users to obtain product_slug from search_beauty_prices and the market list from get_service_info. It does not explicitly list when-not-to-use or alternative tools, but the context is 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?

Annotations cover readOnly/destructive/openWorld, and description adds meaningful context: certifications vs brand claims, and device exceptions. No contradictions. Adds useful behavioral nuance beyond 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?

Description is relatively long but well-structured with clear sections (details, usage, certifications, args). Each sentence adds value, no fluff. Slight over-length but appropriate for 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?

Covers tool purpose, parameters, usage scenarios, and semantic distinctions. Output schema exists, so return values don't need explanation. Adequate for a detail-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?

Schema has 0% description coverage, but description's Args section explains product_slug (source from search_beauty_prices) and country (allowed values, dynamic list via get_service_info). Fully compensates for missing 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?

Description clearly states 'Get full details for one product' and lists specific content (INCI list, key ingredients, images, certifications). Differentiates from siblings like search_beauty_prices (search vs detail) and list_key_ingredients (focused vs full).

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 explicit use cases (answer ingredient questions) and context (devices lack INCI). Distinguishes certifications from brand claims. Does not state when NOT to use, but alternatives are implied through sibling 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?

Annotations already declare readOnlyHint and destructiveHint false, so safety is clear. The description adds value by specifying the content (markets, catalog size, stores, server usage) and the context for use, going beyond the annotations. A slight deduction is warranted because it doesn't mention output format or any additional behavioral details, but it's still strong.

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: the first states precisely what the tool does, the second gives usage guidance. No redundant wording, front-loaded with purpose. Ideal 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 zero parameters and an output schema (not shown but implied), the description clearly outlines the scope of information (markets, catalog size, stores, server usage). It fully addresses what a user would need to know to decide to call this tool. Complete for its simplicity.

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 zero parameterschers, so schema coverage is 100% by default. With no parameters, the description doesn't need to explain themholistic. Baseline 4 applies because no parameter semantics need compensation.

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: describing Glowjakt's markets, catalog size, stores, and server usage. This distinct purpose is well-separated from sibling tools like price comparison or product details, making it obvious when to select this 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?

Explicit usage guidance is provided: 'Call this first if you are unsure which market to query or what Glowjakt is.' This tells the agent exactly when to use it versus alternatives, fulfilling the dimension fully.

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 useful behavioral context beyond that: the exact count of 67 groups, slugs being universal while display names are localized, and the semantic meaning of the 'Controversial' category as the bucket excluded by exclude_controversial=True. It does not contradict the 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 compact and front-loaded: the first sentence states the exact purpose, followed by two short sentences explaining key behaviors, and a concise Args section. Every sentence contributes new information without redundant repetition of the title or schema.

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 single-optional-parameter read-only list tool with an output schema, the description covers everything needed: what it lists, the count, localization behavior, the controversial category relationship, and the valid country values. The output schema handles return-value details, so the description need not repeat them.

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 0%, so the description carries full responsibility for the country parameter. It does this well by listing valid values ('se', 'no', 'dk' or 'fi'), explaining that country controls localized display names, and noting that additional markets may appear and that get_service_info returns the current list. This adds substantial meaning beyond the bare 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 opens with a specific verb and resource: 'List all 67 key-ingredient groups usable as key_ingredient in search_beauty_prices.' This clearly distinguishes the tool from sibling tools like search_beauty_prices and get_service_info by framing it as a reference/enumeration helper rather than a search or price 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 clearly implies when to use the tool: before search_beauty_prices, to obtain valid key_ingredient groups and understand the 'Controversial' avoid-bucket. It also points to get_service_info for the current market list. However, it does not explicitly state when not to use it or name alternatives beyond get_service_info.

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?

Beyond the annotations (readOnlyHint=true, destructiveHint=false), the description discloses key behaviors: EAN-grouped results, exclusion of shipping costs, store_count vs in_stock_store_count semantics, and the verified/claimed distinction for attribute filters. This enriches the agent's understanding significantly and contradicts nothing.

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?

Although lengthy, the description is well-structured with paragraphs and an Args list. Every sentence provides necessary detail, from result grouping to ingredient search examples. It is front-loaded with purpose and avoids redundant fluff.

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 complexity of 9 parameters and the need for result semantics, this description covers all necessary context: EAN-grouping, lowest price behavior, stock count distinctions, shipping exclusions, ingredient search patterns, and guidance to verify claims via get_product_details. The presence of an output schema is complemented by the description's explanation of what the results contain.

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 schema description coverage at 0%, the description fully compensates by documenting all 9 parameters in the Args section. It includes types, defaults, allowed values (e.g., country codes, category list), and complex semantics like comma-separated OR/AND for ingredient and attribute filters, with concrete examples.

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 opening sentence 'Search beauty products and get the lowest current price for each' uses a specific verb and resource, clearly stating the tool's scope. It distinguishes from sibling tools by explicitly contrasting with compare_product_prices for shipping costs and by describing EAN-grouping behavior.

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 states when to use this tool (product search with lowest price) and when to use compare_product_prices for true landed cost. It also provides clear conditions for empty queries with filters, and gives detailed ingredient-aware search patterns that guide the agent on query construction.

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