GolfData

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

With no annotations, the description carries the full burden and handles it well: it discloses variable coverage, that these are advertised rather than verified purchase quotes, that every price carries observed_at and expires_at, and that quoting after expires_at is prohibited. What is missing is auth requirements, pagination or result-size behavior, and latency/cost expectations.

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 short sentences, front-loaded with what is returned before the caveats and the freshness rule. Every sentence carries distinct information — payload, data-quality caveat, staleness rule — though the opening sentence is dense and slightly awkward to parse.

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?

There is no output schema, so the description must describe return content, and it does: min/max/median, 30 and 90 day lows, plus observed_at/expires_at per price. Combined with the data-fidelity caveat, an agent has enough to call and interpret it, though the shape of the returned record set and edge cases (no coverage for a product) go unaddressed.

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?

Only one parameter, and schema coverage is 100% — the schema already explains product_id as a canonical product uuid sourced from search_products. The description's 'for one product' merely restates the singular scope without adding format or sourcing detail beyond the schema. Baseline 3 applies.

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 names the resource (dated advertised retailer observations for one product) and specifies exactly what is returned: min, max, median, and 30/90 day lows. An agent knows this is an aggregated comparison view rather than a raw listing. It does not, however, name or contrast with the obvious siblings get_current_prices or get_price_history, leaving that distinction to inference.

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?

Usage is implied rather than stated: 'these are not exact-variant verified purchase quotes' hints that a verified-offer sibling should be used when exactness matters, and 'do not quote after expires_at' gives a hard validity rule. But there is no explicit when-to-use or when-not-to-use statement and no sibling is named, so the agent must infer the routing.

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 and does so well: it discloses the return artifact (members.27aday.com/go URL), the one-time ticket bound to the caller's API key, the read-only nature ('no purchase or account action happens here'), the cost/auth model ('Free with a key'), and a failure mode ('May return 503 when disabled'). This is unusually rich behavioral context for a no-annotation 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?

Front-loaded with the purpose, then parameter provenance, then return value, then constraints and failure mode. Each sentence carries distinct information (link nature, ticket/API key, retailer handoff, cost, 503), with 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?

No output schema exists, so the description correctly compensates by describing the returned URL and its ticket semantics. Auth expectations (API key), cost, side-effect boundary, and a failure case are all covered, leaving nothing essential an agent needs before invoking it.

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 50% (source_id documented, source_kind's enum values only in the schema). The description adds provenance meaning beyond the schema by explaining both parameters come from the offer's source ids in get_verified_offer(s), which is the key semantic an agent needs to supply valid values.

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?

States a specific verb+resource ('Tracked 27aDay buy link for one marketplace source') and immediately scopes the input to a single marketplace source. It distinguishes itself from siblings by tying source_kind/source_id explicitly to get_verified_offer(s), so an agent can separate it from compare_prices, get_current_prices, get_product, etc.

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 establishes the prerequisite chain ('source_kind and source_id from get_verified_offer(s), the offer's source ids') and tells the agent what to do with the result ('hand it to the shopper unchanged'). It does not name an explicit alternative tool or state when this should be skipped, but the workflow 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?

With no annotations, the description carries the full behavioral burden and discloses important traits: prices are stored/advertised rather than verified, include stock observations and affiliate links, and carry observed_at/expires_at with an expiry quoting rule. It does not cover read-only status, auth, or rate limits, so it is strong but not exhaustive.

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 tight sentences: purpose first, differentiation second, expiry caveat third. Every sentence adds a distinct constraint and there is 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 one-parameter read tool with no annotations or output schema, the description covers what is returned, how fresh it is, and when to prefer an alternative. It still leaves the exact return shape and any pagination/error behavior unspecified, so it is not quite 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?

The single required parameter is fully documented in the schema as a canonical product uuid from search_products. The description only restates 'one canonical product' and adds no syntax, format, or sourcing detail beyond the schema, so the baseline 3 applies.

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?

States it returns latest stored advertised prices for one canonical product, with stock observations and stored affiliate links. It explicitly distinguishes itself from get_verified_offer, making the scope and resource clear enough to separate it from 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?

Explicitly tells the agent to use get_verified_offer when a marketplace source ID is known and warns that these are not verified buyable offers. It also adds a quoting constraint ('do not quote after expires_at'), giving clear when/when-not 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 and does well: discloses coverage/refresh variability, that rows are asking prices not proof of discount or stock, and that quotes require expires_at vigilance. It adds meaningful caveats about data freshness and reliability beyond the 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 tight sentences, each earning its place: what the data is, its variability, and the critical expiry caveat. Front-loaded purpose and 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?

Covers freshness caveats well, but for a history tool with no output schema, it omits return shape (e.g., per-retailer rows, fields like price/currency) and lacks explicit routing vs get_current_prices. Adequate but with clear gaps.

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% with both parameters documented, including product_id source and days range/default. The description adds no parameter-level meaning beyond the schema, so 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 states it returns dated price observations per retailer, a specific resource with temporal scoping. It implicitly contrasts with get_current_prices by emphasizing 'dated' and 'history', but does not explicitly name the sibling it differs from.

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?

Usage is implied: use for historical prices, noting variability and expiry. No explicit when/when-not guidance or reference to alternatives like get_current_prices or compare_prices despite clear siblings.

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 burden and does well: it declares read-only behavior, that no account actions/click attribution/purchase occur, that deployment may return 503, and that prices carry observed_at/expires_at with a do-not-quote rule. It omits auth requirements and rate limits, so it is not exhaustive.

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?

Front-loads the read-only/gate nature and packs prerequisites, return modes, failure mode and expiry semantics into a compact set of sentences with little waste. The internal jargon ('Chip's admission', 'live redirect gate') costs clarity without adding much for an outside agent.

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 and no annotations, the description supplies the return modes (verified_offer, check_price, unavailable), unknown-value behavior, the 503 deployment caveat and expiry rules, which is a lot of the missing structure. Gaps remain around the meaning of 'check_price' and the source_kind deal/product choice.

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%, so the schema already documents source_id, variant_id and canonical_product_id; the description usefully adds that explicit variant_id null means no selectable variant and reinforces the source-vs-canonical ID distinction. It adds nothing for the undocumented source_kind enum (deal/product), leaving that 25% gap unfilled.

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?

States a specific read-only resource (a verified golf offer) and scopes it to a single source deal/product with an exact variant, which is distinct enough from the plural sibling get_verified_offers. However, the leading jargon ('golf-offer.v1 using Chip's admission, exact-variant and live redirect gate') is opaque and the singular-vs-plural distinction from get_verified_offers is never made explicit.

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?

Gives a concrete prerequisite and routing hint: it requires a known 27aDay marketplace source UUID and warns that search_products returns canonical IDs rather than these source IDs, so the agent learns where the needed input does NOT come from. It stops short of saying when to prefer this over get_verified_offers 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 and does: bounded 16-source live window, two sources per page, non-exhaustive/non-snapshot semantics, 503 when deployment is disabled, no member actions or click attribution, and observed_at/expires_at staleness rules on every price.

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?

Front-loaded with the purpose and dependency, and the dense operational clauses (window, offsets, staleness, 503) each earn their place. It is long, but the length is justified by the number of distinct behaviors an agent must know.

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 complex, non-snapshot, gated read tool with no output schema, the description covers return shape (golf-offer.v1, check_price, unknown currency), pagination, staleness, and failure modes. Nothing material an agent needs before calling is missing.

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 three parameters are already documented in the schema, including variant_id null behavior and the offset bounds. The description restates the next_offset/window semantics and adds little syntax beyond what the schema provides, so the baseline 3 applies.

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?

States a specific verb chain (discover sources, then verify through the commerce gate) tied to a canonical product ID sourced from search_products. An agent can tell this is the batch verification path and that it is gated rather than a raw catalog read.

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 the agent to supply the exact variant UUID to get offers and that omitting variant_id yields selection metadata instead, plus directs use of next_offset. It does not, however, route against the singular sibling get_verified_offer, leaving the batch-vs-single choice implicit.

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.