glowjakt

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

Annotations already mark this as read-only, and the description adds valuable context: it returns every store offer with price, stock status, and direct buy link, ordered cheapest first. It does not address edge cases like unavailable products, but with annotations covering safety, this is adequate.

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 well-structured: a one-sentence purpose, a return-value summary, a prerequisite note, and a clear parameter list. No unnecessary words; every sentence contributes to understanding the tool.

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 simple interface with two parameters and an output schema, and the description covers purpose, output ordering, key fields, and the parameter source. It is fully complete for the tool's complexity and existing structured metadata.

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 0%, so the description fully compensates by explaining product_slug as the value from search_beauty_prices and country as one of four specified markets. This gives the agent all necessary information to set parameters correctly.

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 prices for one product across all stores, with a specific verb and resource. It distinguishes itself from siblings like search_beauty_prices (which finds products) and get_price_history (which likely shows historical trends) by focusing on current store offers.

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 clear prerequisite: obtain product_slug from search_beauty_prices. This implies the tool is used after searching for a product, though it does not explicitly list alternative tools or state when not to use it, so it misses the highest bar.

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 openWorldHint=false, covering the safety profile. The description adds valuable beyond-annotation context: discounts are computed against real previous prices and stores with inflated recommended prices are filtered out. This explains the 'genuine' aspect and sorting behavior, exceeding what annotations alone provide.

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, front-loading a clear one-sentence summary, followed by a brief but informative note on discount methodology, and then a compact Args list. Every part serves a purpose with no unnecessary repetition 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 the annotations, output schema presence, and parameter complexity, the description covers the essential aspects: purpose, parameter constraints, sorting behavior, and the discount authenticity filter. The output schema presumably handles return structure, so nothing critical is missing for a read-only deal-finding 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 0%, so the description fully compensates with an explicit Args section. It lists allowed country values ('se', 'no', 'dk', 'fi'), category options, min_discount with an example, and the limit range (1-20), adding crucial meaning beyond the bare schema types and defaults.

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

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

The description uses a specific verb 'find' and a clear resource ('beauty products currently on sale'), and it distinguishes itself from siblings by emphasizing 'biggest genuine discounts first' and a unique discount computation method. This is far more specific than a generic 'find' and clearly sets it apart from price comparison or history tools.

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

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

The description implies its use case (finding deals) through the summary and parameter details, but it does not explicitly state when to use this tool versus alternatives like search_beauty_prices or compare_product_prices. There is no explicit exclusion or reference to sibling tools, leaving the agent to infer the decision.

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, and the description adds behavioral context by specifying 'lowest price per day' and noting that product_slug comes from search_beauty_prices. It also enumerates valid country values.

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 3 short paragraphs, front-loaded with purpose, and every sentence adds value.

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

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

With an output schema present and read-only annotations, the description covers purpose, usage, and parameters adequately. It doesn't need to explain return values.

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 no descriptions (0% coverage), so the description must compensate. Both parameters are explained: product_slug's source is given, and country's allowed values are listed.

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 'Get the recent price history (lowest price per day) for a product' with a specific verb and resource, clearly differentiating from siblings like compare_product_prices or find_deals.

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 explicitly provides example questions ('is this a good deal right now?' or 'has the price dropped recently?') that indicate when to use it, but it doesn't mention alternatives or exclusions.

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

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

Description adds behavioral depth beyond readOnlyHint: distinguishes third-party certifications from unverified brand claims, instructing to always present brand claims as claims. This is rich context for agent hallucination prevention.

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?

Well-structured: opening summary, usage context, data semantics, then Args. Front-loaded with purpose; no 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?

Despite only 2 params and an output schema, description gives usage guidance, parameter provenance, edge-case handling (non-cosmetic items), and critical data-interpretation rules. Fully 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 has 0% description coverage, but description compensates: product_slug is specified as coming from search_beauty_prices, and country lists allowed values 'se', 'no', 'dk', 'fi'. This adds meaning beyond 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?

Description clearly states 'Get full details for one product: INCI ingredient list, key ingredients, description, product images and brand-level certifications.' This is a specific verb+resource+scope, distinguishing it from pricing/deals 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?

It provides explicit use cases: 'Use this to answer ingredient questions like...' and clarifies that devices/accessories lack INCI lists. However, it does not name alternative tools for exclusions (e.g., list_key_ingredients), so slightly below 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=true, and the description adds context about the informational scope (markets, catalog size, stores). It does not disclose additional behavioral traits like response size or performance, but for a read-only overview tool the description is adequate and 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?

Two efficient sentences: the first states purpose, the second gives usage context. Every word contributes; no 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?

With no required parameters and an output schema present, the description fully covers what the tool does and when to use it. As a first-call onboarding tool, it is complete for its intended role.

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?

Tool has zero parameters, so baseline is 4. Description adds no parameter-specific details because none exist to document.

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 uses a specific verb 'Describe' with a clear resource (Glowjakt: markets, catalog size, stores). It clearly distinguishes from sibling tools by positioning itself as an introductory/overview 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?

Explicitly tells when to use: 'Call this first if you are unsure which market to query or what Glowjakt is.' This is strong guidance, but it does not mention when-not-to-use or alternatives beyond this context, so it misses full marks.

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 openWorldHint=false, so the safety profile is known. The description adds valuable behavioral context: the exact count (67), that slugs are universal while display names are localized, and the meaning of the 'Controversial' bucket. This goes beyond the annotations without contradicting them.

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, front-loaded with the main purpose, and uses a clean separation with line breaks for the parameter. Every sentence adds value—no fluff or repetition. It is appropriately sized for a simple tool.

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 listing tool with one optional parameter, the description covers all necessary context: exact enumeration, localization, the controversial category, and parameter semantics. The existence of an output schema means return values don't need to be described, and the description is complete within that scope.

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 0% schema description coverage, the description fully compensates by explaining the single 'country' parameter: it is for localized display names, with allowed values 'se', 'no', 'dk' or 'fi'. This adds meaning beyond the bare type/default in the schema, making parameter usage clear.

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 lists all 67 key-ingredient groups, with a specific verb ('List') and resource ('key-ingredient groups'). It also distinguishes itself by specifying these are usable as key_ingredient in search_beauty_prices, which sets it apart from sibling tools like get_price_history or find_deals.

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 for when to use this tool: when you need a valid key_ingredient for search_beauty_prices. It also explains the 'Controversial' category relationship to exclude_controversial. However, it doesn't explicitly state when not to use it or mention alternative tools, so it stops 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?

Beyond the readOnlyHint annotation, the description discloses key behavioral traits: results are EAN-grouped at the lowest price across stores, store_count indicates seller count, ingredient filters use OR/AND semantics, and exclude_controversial expands to a specific list of ingredients. It also warns that attribute matches may be brand claims and to verify via get_product_details before presenting as fact.

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, followed by a brief EAN-grouping note and illustrative examples, then a well-organized Args list. Every sentence carries essential operational detail; the length is justified given the tool's parameter complexity and semantic richness, with 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?

For a tool with 9 parameters and complex filter behavior, the description leaves no gaps: it covers all parameter semantics, edge cases (OR/AND, exclude vs. key), and cross-references sibling tools for supporting information. An output schema exists, so return value details are not required; the description fully satisfies contextual 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?

Even though schema description coverage is 0%, the description provides explicit meaning for every parameter in the Args section, including defaults, allowed values (e.g., country codes, category list), filter semantics (comma-separated = OR, attribute = AND), and concrete examples like 'moisturizer with retinol'. This fully compensates for the schema's lack of 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 opens with a specific verb-resource pair: 'Search beauty products and get the lowest current price for each.' It further distinguishes the tool from siblings by explaining EAN-grouping and the store_count field, making it clear this is a product search with price aggregation rather than comparison, deals, or 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?

The description provides clear context for when to use the tool: product searches with optional filters, including examples of ingredient-aware queries. It explicitly points to list_key_ingredients for valid slugs and get_product_details for verified/claimed distinctions, giving some alternative guidance, though it does not explicitly contrast with compare_product_prices or find_deals.

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