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 transparently discloses the tool's output scope, including the nuance that CaddenceScore rates the 'current best DEAL, not the club', and lists all data fields returned. It does not explicitly state read-only behavior or limitations, but the 'assessment' framing implies a safe read 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 a single dense sentence that packs many useful details without redundancy. It is front-loaded with the core purpose and uses a list-like structure. It is longer than strictly necessary but each clause adds value, so it earns a 4.

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 output schema, the description does a solid job explaining return values: CaddenceScore range, price trajectory, market stats, per-build availability, cheapest retailer, and buy_url. It misses explicit guidance on interpreting scores or handling missing data, but is complete enough for an agent to use the tool effectively.

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 reinforces that the product is for a single product, but the schema already fully documents 'product' and 'context' parameters, so the description adds little beyond what is already structured.

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

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

The description clearly identifies the tool as 'Deal-quality assessment for one product' and enumerates specific outputs: CaddenceScore (0-100), price trajectory, used market stats, per-build availability with cheapest retailer and buy_url. It explicitly states the tool's purpose to answer 'is this a good price right now?', which distinguishes it from sibling tools like get_price_history or get_market_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 a clear when-to-use context: 'Use this to answer "is this a good price right now?"'. However, it does not explicitly mention when not to use it or name alternative tools, so it falls 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?

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 burden and does disclose the variant fallback behavior and the existence of a 'scope' field. However, it does not cover other behavioral aspects such as rate limits, data freshness, or whether the tool is read-only, leaving gaps.

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

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

Two well-structured sentences front-load the core purpose and add one important behavioral note about fallback. No wasted words; information density is high.

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 7 parameters and no output schema, the description covers the essential behavior: daily prices, summary stats, and variant fallback. It does not fully describe all parameters like condition or the exact response structure beyond 'scope', but it is reasonably complete.

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

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

The description adds meaning beyond the schema by explaining 'trailing window' (days), variant configuration (likely flex/hand/loft), and the variant-to-product fallback, which clarifies the scope field. This partially compensates for the 57% schema description coverage.

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 returns daily lowest/average UK prices for one product over a trailing window, plus summary stats, distinguishing it from sibling tools like get_product_offers or search_golf_products. The verb 'get' is implicit, but the resource and scope are specific.

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 historical price analysis and mentions optional variant scoping with fallback, but does not explicitly state when to use this tool over alternatives or when not to use it. No exclusions or alternative tool names are mentioned.

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 of behavioral disclosure. It states ordering (cheapest first), the presence of variant details, and the critical rule that buy_url is a Caddence redirect link that must always be used for purchases, with an explicit 'never reconstruct or substitute retailer URLs' constraint. This is substantive 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 highly concise: two sentences that front-load the core function and then deliver an essential instruction. Every sentence adds value, with no redundant or vague wording.

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 offer-lookup tool, the description adequately covers scope (UK, current, one product), ordering, variant details, and the buy_url behavior. It does not describe the full return format, but in the absence of an output schema, it still provides sufficient operational context for an agent to use the tool effectively.

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 already covers 100% of parameters with detailed descriptions, including product slug/id preference and the context string requirements. The tool description does not add additional parameter semantics; it focuses on output behavior rather than input meaning. Baseline 3 is appropriate.

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

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

The description clearly states the tool returns all current UK retailer offers for one product, ordered cheapest first, with specific variant details (shaft, flex, hand, loft, condition). It differentiates from siblings like get_price_history or get_market_deals by focusing on current individual product 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 clearly implies when to use this tool: when current UK retailer offers for a specific product are needed. It provides a strong usage constraint about always presenting buy_url as the purchase link. It does not explicitly mention when not to use alternatives, but the sibling tool set makes the distinction obvious.

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.