caddence

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

With no annotations, the description carries the burden of behavioral context. It discloses that the data is 'live' and specifies the exact categories of valid filter values, giving a realistic sense of what the tool returns. It does not explicitly mention read-only behavior, but 'stats' implies it.

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

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

The description is two sentences long, each serving a distinct purpose: the first states what the tool does, the second provides usage guidance. No wasted words.

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

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

For a simple one-parameter tool with no output schema, the description adequately covers the tool's function and usage. It lists the filter types included, but does not specify the format of the return value or the exact meaning of 'catalogue totals'.

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 covers the only parameter ('context') with a full description, so the baseline is 3. The tool description does not add any additional semantic information about the parameter, making it sufficient but not enhanced.

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 specifies what the tool does: returns live catalogue totals and valid filter values for brands, product types, conditions, flexes, hands, and price range. This is specific and distinguishes it from sibling tools like get_market_deals or get_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?

The description explicitly advises to call this tool before filtered searches when unsure about catalog-specific spellings, which provides a clear when-to-use directive and indirectly references alternatives like search_golf_products.

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 clearly indicates this is a read-only data retrieval operation that returns a numeric score, price stats, and per-build details. It does not explicitly state that no data is mutated, but the nature of 'assessment' and 'stats' strongly implies read-only. It could be more explicit about potential API rate limits or data freshness.

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, well-structured sentence that front-loads the core purpose ('Deal-quality assessment for one product: CaddenceScore') and then enumerates the key outputs. Every clause adds specific information without repetition or filler. It efficiently captures purpose, output details, and usage context in under 50 words.

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

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

Given the lack of output schema and lack of annotations, the description covers the tool's return value details (score, price stats, per-build data, buy link) well, which is essential for an agent to understand what results to expect. However, it does not explain how the CaddenceScore is calculated or mention edge cases (e.g., what if no deal exists?). A brief note on the scoring criteria would elevate 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 describes the three parameters. The description adds value by explaining slug and product are aliases and that the tool returns data for one product. However, the description does not elaborate on the parameters beyond what the schema provides; the context parameter is detailed in the schema, but the description adds no extra semantics about slug or product 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 clearly specifies the tool's purpose: providing a deal-quality assessment called CaddenceScore (0-100 rating) for one product. It explicitly lists the output components (price trajectory, used market stats, per-build availability with cheapest retailer and buy URL), distinguishing it from generic search or pricing tools. The 'current best DEAL, not the club' clarification adds precision.

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 direct usage query example: 'Use this to answer "is this a good price right now?"' This tells the agent exactly when to invoke the tool. Considering sibling tools like get_price_history and get_market_deals, this description implicitly differentiates itself as the tool for aggregated deal scoring rather than raw price lists or market overviews.

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?

There are no annotations, so the description must carry the burden. It mentions 'verified price spreads', '3+ retailers', and 'false-positive filters applied', which provides some insight into the tool's underlying logic. However, it doesn't describe the return format or any safety characteristics beyond being read-only by implication.

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, both packed with relevant information. There is no redundancy or filler, making it highly efficient.

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 provides enough context for a simple list tool: it explains the deal logic, offers an example query, and points to a related tool. It doesn't fully describe the output format, but for this tool, the return type is straightforward. The lack of an output schema raises the need for some return info, but the description gives a decent overview.

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 describes 'context' and 'category' but leaves 'limit' without a description. The tool description doesn't mention any parameters or add further semantic meaning, so the agent must rely solely on the schema. Since coverage is at 67%, the description doesn't compensate for the missing 'limit' parameter.

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 returns 'Current best-value products across the UK market' with verified price spreads, and provides an example use case. It distinguishes itself from siblings by suggesting a follow-up with get_product_offers for purchase links.

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

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

The description explicitly says 'Good for "what are the best golf deals right now?"' which tells the agent when to use it. It also advises following up with get_product_offers for purchase links, implying when to use that sibling instead. However, it doesn't explicitly exclude other scenarios or mention alternatives like get_deal_intelligence.

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 burden. It discloses key behaviors: trailing window, optional variant scoping, and backend fallback to product-level history when variant data is thin. This adds valuable transparency beyond the schema, though it omits details like error handling or authentication.

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 sentence covering main output, optional scoping, and fallback. It is concise but contains a misleading reference to 'scope field' that does not exist in the input schema, which harms clarity. Front-loading is adequate.

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 insufficient for an 8-parameter tool with no output schema and no annotations. It does not explain the output format, error conditions, or the meaning of parameters like condition or days. The summary stats are mentioned but not structured, leaving the agent guessing.

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 63%, so the baseline is 3. The description adds context about variant scoping and fallback, but does not explain individual parameters (e.g., flex, hand, loft) beyond what the schema already provides. The reference to a missing 'scope field' reduces clarity.

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 daily lowest/average UK price for one product over a trailing window, plus summary stats. It distinguishes from sibling tools like get_catalog_stats or get_product_offers. However, it references a non-existent 'scope field' in the schema, causing minor confusion.

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 price history but offers no explicit guidance on when to use this tool vs alternatives like get_deal_intelligence or get_market_deals. The context of sibling tools suggests differentiation, but the description itself lacks direct usage directions.

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?

Without annotations, the description discloses sorting (cheapest first), geographic scope (UK only), and the inclusion of variant details. It also reveals that offers are current (not historical). However, it does not mention pagination limits, rate limits, or whether offers are cached, leaving gaps in behavioral transparency.

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

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

The description is two sentences long and front-loads key information (scope, sorting, variant details). The second sentence on buy_url usage is essential but could be slightly more concise. Overall, 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?

Given the tool has 3 parameters, no output schema, and no annotations, the description could be more complete. It lacks information about response format, error cases (e.g., product not found), and whether historical offers are included. The buy_url guidance is helpful but doesn't fully compensate for missing behavioral details.

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%, but the description does not clarify the difference between slug and product parameters beyond what the schema already states. The context parameter is well-documented in the schema with detailed instructions, so the description adds minimal value here.

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 will return 'All current UK retailer offers for one product, cheapest first, with variant details'. It distinguishes itself from sibling tools by mentioning specific fields like shaft, flex, hand, loft, condition, and the inclusion of buy_url links, which no other sibling tool addresses.

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 explicit guidance on presenting buy_url as the purchase link and warns against reconstructing retailer URLs. However, it does not explicitly state when to use this tool versus alternatives like get_price_history or get_deal_intelligence for different offer contexts.

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 burden and handles it well: it discloses daily price refresh, GBP currency, and a key caveat ('When in_stock is false, price is the last seen price, not a live offer'). It does not cover all output detail, but adds essential 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?

The description is four concise sentences, each adding distinct value: scope, returns, caveat, and follow-up instructions. No filler or redundant wording, and key information 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?

Given 11 parameters and no output schema, the description gives a solid overview, mentions the returned slug, and highlights the availability caveat. However, it lacks explicit pagination behavior (offset/limit) and a fuller picture of result fields beyond 'best current prices with retailer counts', so some gaps remain.

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 64%, leaving several parameters undocumented (limit, offset, sort_by, condition). The description adds context about GBP and the in_stock/last-seen-price behavior, but does not clarify the undocumented parameters or their interplay. It points to get_catalog_stats for valid values, which partially compensates.

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 searches Caddence's UK golf equipment price-comparison catalogue, listing product categories and scope. It distinguishes from siblings by explicitly noting follow-up with get_product_offers and get_catalog_stats, making the tool's unique role unambiguous.

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 usage guidance: 'For retailer offers and purchase links, follow up with get_product_offers using the returned slug' and 'If unsure of valid brand or product_type values, call get_catalog_stats first.' This directly instructs when to use alternatives, going beyond generic context.

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