GolfData

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

With no annotations, the description carries the burden and does a good job: it discloses that the call returns current prices plus summary statistics and low windows. It does not mention response shape or freshness caveats, but the core behavior is clear.

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 with no filler; the output summary is front-loaded and the purpose/usage tag is 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?

For a single-parameter read tool with no output schema, the description names the key return values and the intended decision. It is sufficiently complete to invoke correctly, though it leaves minor details like result formatting 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?

The schema already documents product_id fully as 'canonical product uuid, from search_products', so parameter coverage is 100%. The description adds only the 'one product' scope and the purpose, which is minor on top of the 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 states exactly what the tool returns: current retailer prices, min/max/median, and 30/90-day lows, tied to one product. The closing phrase 'The single call to answer...' differentiates it from siblings like get_current_prices and 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 'single call to answer' line gives a clear when-to-use signal for the price-goodness question. It does not explicitly name sibling tools or state when not to use it, so it falls just short of full guidance.

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 of explaining behavior. It discloses the data returned, the stored/non-verified nature of the prices, and the limitation that these are not exact-variant buyable offers. It could add more about freshness or response shape, but it is substantially transparent for a simple getter.

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 with no filler. The core action and contained data are front-loaded, and the caveat plus alternative routing are placed in the second sentence. Every clause 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?

For a single-parameter tool with no output schema, the description explains what the result contains, what it excludes, and when to choose an alternative. It stops short of describing the exact return shape or potential failure modes, but an agent has enough context to invoke it 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 coverage is 100%, so the schema already documents product_id as the canonical product uuid from search_products. The description adds little beyond the phrase 'one canonical product', which is already implied by the parameter description. 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 uses specific language: it retrieves the latest stored advertised prices, stock observations, and affiliate links for one canonical product. It also explicitly distinguishes itself from verified-offer tools, so an agent can tell it apart from siblings without reading schemas.

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 gives a clear when-to-use rule: use this when you need stored advertised prices for a canonical product, and switch to get_verified_offer when a marketplace source ID is known. The explicit 'not exact-variant verified buyable offers' caveat also prevents misuse.

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 of behavioral disclosure. It mentions unbroken snapshots since a specific date, which is useful context. However, it does not disclose data volume limits, pagination, or the structure of the returned series. The description adds some behavioral context but is not comprehensive for a data retrieval tool.

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 (one sentence) and front-loads the core purpose. However, it also includes an implied usage hint and a data quality note, which are relevant but not structured. Overall, it is efficient with no filler 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 retrieval tool with 2 parameters and no output schema, the description is moderately complete: it explains the data scope and the interpretative value. But it lacks any mention of the response format, data volume, or how to handle edge cases, which could be necessary for the agent to handle results correctly. With no output schema, more detail on return value could be expected.

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 both parameters (days, product_id) are already described in the schema. The description does not add new meaning beyond what the schema provides; it reinforces the product_id and the historical nature but adds no additional syntax or format details. 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 retrieves daily price snapshots per retailer, which is a specific verb+resource. It does not explicitly differentiate from siblings like get_current_prices, but it adds context about the data source and temporal coverage. The core purpose is clear, but sibling differentiation is only implicit.

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?

No explicit guidance on when to use this tool versus alternatives like get_current_prices or compare_prices. The description implies it is for historical analysis ('whether a discount is genuine'), but does not state when not to use it or mention alternatives. The agent is left to infer usage from the context.

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 of behavioral disclosure. It explains what the response contains, the provenance attributes on enriched fields, and the meaning of enrichment_status values, which is substantial context beyond the raw schema.

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 focused sentences convey the core behavior, provenance model, and enrichment-status semantics without wasted words. The most important message, what the tool returns, 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?

For a single-parameter retrieval tool with no output schema, the description covers the key behavioral details: return scope, provenance fields, and status interpretation. Minor gaps like missing-product behavior are not critical given the low complexity.

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 already fully describes product_id as a canonical product UUID from search_products, and the description adds no parameter-specific detail. With 100% schema description coverage, 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 states a clear verb and resource: retrieving one product plus its enrichment provenance rows. It implies differentiation from siblings like search_products by emphasizing a single product and provenance detail, though it does not explicitly name alternatives.

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 valuable guidance about checking enrichment_status before trusting specs, which informs how to interpret results. However, it does not explicitly state when to choose this tool versus siblings like get_current_prices or get_verified_offers.

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 burden and does well: it declares read-only behavior, no account actions/click attribution/purchase, possible outputs, handling of unknown currency/ratings, and a possible 503 when deployment is disabled. This is strong transparency, though 'Chip's admission' and 'live redirect gate' remain unexplained jargon.

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 with read-only intent, then moves to requirements, parameter nuance, and behavioral caveats. It contains valuable information without excess, though a few domain-specific terms reduce immediate clarity.

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 four required parameters, no output schema, and no annotations, the description covers the key operational aspects: input source distinction, null handling, return statuses, side-effect absence, and deployment risk. It could be more complete by explaining the admission gate and how to handle 503, but it is largely self-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 coverage is 75%, and the description adds meaningful distinctions beyond the schema: source_id is a marketplace source UUID rather than canonical, canonical_product_id comes from search_products, and explicit variant_id null means no selectable variant. It does not elaborate on source_kind, but the core parameter semantics are well supplemented.

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 identifies a specific operation ('Read-only golf-offer.v1'), names the resource type (marketplace deal/product source UUID), and clarifies the expected outputs ('verified_offer, check_price or unavailable'). It does not explicitly contrast itself with sibling get_verified_offers, but the singular/exact-variant phrasing implies the distinction.

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 usage requirements: requires a known source UUID rather than canonical product IDs, and explains that search_products yields canonical IDs, not source IDs. Also clarifies variant_id null semantics, giving agents actionable selection criteria, though it does not list specific when-not-to-use conditions or 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?

With no annotations provided, the description carries full behavioral disclosure burden. It goes beyond a simple API summary to reveal meaningful behaviors: variant_id omission yields selection_metadata_not_offer options, only two sources per page are checked within a bounded 16-source window, results are not a stable snapshot, data retains golf-offer.v1, there are no member actions or click attribution, and deployment may return 503. This is rich, useful behavioral information.

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 but dense, with each sentence adding meaningful information. It front-loads the core purpose before caveats and edge cases. Some jargon like 'golf-offer.v1' and 'exact-source commerce gate' is unexplained, but the overall structure is organized and avoids 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?

Given the absence of annotations and output schema, the description supplies substantial context: usage prerequisites, variant behavior, pagination limits, result format version, and deployment failure mode. It is adequate for an agent to call the tool correctly, though a bit more detail on the actual shape of returned offers would push it to fully 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 description coverage is 100%, so the baseline is 3. The description adds value beyond schema by clarifying the behavioral consequence of omitting variant_id on variant-bearing products and emphasizing that canonical_product_id comes from search_products. It also reinforces the meaning of offset within the bounded live window, enriching the schema's basic definitions.

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 discovering and verifying marketplace offers for a canonical product ID, using the verb 'discover' and 'verify' with a specific resource. It also distinguishes itself from siblings by describing the plural offer verification behavior and 'exact-source commerce gate', which is not present in sibling names like get_current_prices or get_verified_offer.

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 when to use the tool: after getting a canonical_product_id from search_productsata, and when exact variant UUIDs are needed. It gives conditional guidance for variant-bearing products and advises to use next_offset for pagination. However, it does not explicitly state alternatives or when not to use this tool versus siblings like get_verified_offer or get_current_prices.

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 that the search matches free text against product names, and lists categories, but it doesn't detail behavior like case sensitivity for categories, whether the search is fuzzy or exact, or the return format. The basic behavior is clear, but richer details are missing.

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, with the core purpose front-loaded. The category list is a necessary addition for this domain-specific tool. Every sentence serves a purpose: the first defines the tool's function, the second gives usage guidance, and the third lists valid values. No waste.

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's role as a search entry point with an output schema absentjon, the description doesn't explain the return format or selection logic, which could be important. However, the purpose and basic parameters are covered, and the sibling tools suggest how results are used. It's adequate but not thorough.

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 all parameters are documented in the schema. The description adds the list of categories, which is also in the schema, and the 'free text matched against the product name' with the 'call this first' context. This adds minimal value beyond the schema, so a 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's purpose: to search the golf equipment catalog by brand, category, or free text, and to resolve product IDs for other tools. This is specific about the resource (golf equipment catalog) and the action (search), and it distinguishes itself from siblings like get_product by its search role.

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 instructs to call this tool first to resolve product_id for other tools, providing clear when-to-use guidance. It doesn't explicitly mention when not to use it or alternatives, but the context of 'first step' implies it's the entry point, and sibling tools are for specific follow-ups, which is clear enough.

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