GolfPrice AI

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

With no annotations, the description carries the full burden and does a fair job: it discloses brand neutrality, a 6-hour price refresh cadence, the exact ranking logic (lowest-ever tracked price first, then largest drop below tracked high), and conditional inclusion of a budget-guide link. It omits auth/permission needs and behavior when nothing matches the budget.

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 examples are front-loaded effectively, but the core sentence is a long run-on with a densely nested ranking parenthetical that is hard to parse on first read. The ranking detail is useful but could be broken into a shorter, cleaner statement.

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 still conveys what a result set looks like: models of the requested type plus a budget-guide link when available, ordered by the stated ranking. It is nearly complete, missing only result field detail and empty-result behavior.

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 category, max_price and limit are already documented, making 3 the baseline. The description adds modest value by clarifying that the budget filter is applied to each model's current lowest new UK price, but it offers no extra detail on limit or category beyond what the schema states.

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 specific verb+resource: it returns every tracked model of a given category whose current lowest new UK price falls within a budget, ranked by value. This clearly separates it from lookup-oriented siblings like get_golf_price and compare_golf_prices, though it never names those siblings explicitly.

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 opening examples ("best driver under £300", "golf balls under £30 a dozen") give concrete triggers for when this tool is the right call, and the budget framing implies it is the discovery tool rather than the single-price or comparison tools. No explicit when-not or named alternatives are provided.

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 discloses the returned facts: lowest new price today, retailer counts, tracked low/high/average, verdicts, and comparison page when available. It also states the price-only limitation, but does not explicitly confirm read-only behavior or error handling.

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 comparison purpose and output facts, then gives the input source and scope limitation. Every sentence adds value, and the description is appropriately sized.

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 explains the return content well and notes the comparison page is conditional. It is nearly complete, but omits explicit read-only/error behavior, which matters slightly more given the absence of annotations.

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 examples, so the baseline is 3. The description adds useful provenance by instructing the agent to use model_ids from search_golf_prices, which goes beyond the schema's example strings.

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 and resource: side-by-side UK price comparison for two golf models, with the exact output facts listed. It distinguishes itself from search_golf_prices by requiring model_ids from that tool and explicitly limits scope to prices, not performance.

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?

Clearly says to use model_ids from search_golf_prices and that the tool compares prices only, not performance. However, it does not name alternatives for single-model lookup (get_golf_price) or budget-based browsing (best_golf_under_budget).

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 behavioral burden well: it discloses the data source (OpenStreetMap), approximate coverage (about 2,800 courses), and what each match includes. It could be richer about matching behavior or result limits, but it provides substantial operational context for a search 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 definition is two tight sentences with the core action, scope, source, and return value front-loaded. The use-case examples are efficiently packed at the end without unnecessary repetition.

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 search tool with no output schema, the description is complete enough: it explains the data source, searchable fields, return fields, and when to use it. No critical calling information 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% and the single parameter is already documented with examples such as "Harrogate" and "Oakdale." The description says the query may be a course name or town, but that does not add meaning beyond 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 a specific verb and resource: it searches every UK golf course by name or town and returns matches with town, nation, and a GolfPrice AI course page link. The scope and return content make it clearly distinguishable from sibling price, society, and handicap 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?

It explicitly lists good use cases such as "golf courses in Harrogate" and "is there a golf course called X," which gives the agent clear triggering contexts. However, it does not explicitly say when not to use it or name alternatives like search_golf_prices for price-focused queries.

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 load and does a good job: it names the data source, discloses the 6-hour refresh cadence, and specifies which records are returned (only societies marked open to new members). It omits ordering or result-count behavior, but the coverage constraint is a genuinely useful caveat.

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 resource and search keys, then the payload, then freshness, then audience — a sensible order with no filler sentences. The searchable-by clause duplicates the schema slightly, costing a small amount of tightness.

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, and the description compensates fully by enumerating the returned fields (area, description, member count, join link) plus data freshness and record eligibility. An agent has everything needed to decide relevance and call it with or without the optional filter.

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% and the single parameter already documents that it matches town, county or society name and that empty lists all. The description restates the same matching semantics without adding format, partial-match, or ambiguity-resolution detail, so it is baseline value over 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?

States a specific resource (UK golf societies listed as open to new members) and its scope (searchable by town, county or name), with the return fields spelled out. It does not explicitly differentiate itself from siblings like golf_society_guide, but the directory-lookup framing is distinct enough for an agent to pick correctly.

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?

"Useful for golfers who want regular golf without a full club membership" implies the audience and trigger, but there is no when-not-to-use guidance and no named alternative among the golf_society_* siblings. The agent must infer routing from the purpose statement alone.

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 exist, so the description carries the full burden, and it does substantial work: it discloses the exact payload (lowest new UK price and retailer, all tracked retailers, min/max/avg plus day counts, and a buy-timing verdict). It omits auth requirements, freshness/caching cadence, and invalid-model_id behavior, which keeps it out of 5 territory.

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?

Purpose is front-loaded in the first clause, followed by a single dense enumeration of return contents that each earn their place given there is no output schema. The long compound sentence is information-rich but slightly heavy 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?

With no annotations and no output schema, the description must cover return values itself, and it comprehensively enumerates them. For a read-only, single-parameter lookup tool, nothing essential to correct invocation 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 coverage is 100% and the single parameter already has a concrete example, so the schema does the heavy lifting. The description still adds meaning the schema cannot: the provenance of the value (it must come from search_golf_prices) rather than being freely invented.

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 ('Full GolfPrice AI price facts for one model') and enumerates exactly what is returned. It distinguishes itself from the discovery sibling by instructing the agent to source model_id from search_golf_prices, so an agent can tell it apart from search_golf_prices without opening either schema.

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 where the required model_id comes from ('Use a model_id from search_golf_prices'), establishing the discovery-then-detail workflow. It does not state when-not to use it or what golf_price_drops offers by comparison, so no explicit exclusion/alternative 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 load and does well: it discloses the rounding rule, the WHS table used for fewer than 20 scores, and explicitly bounds scope with 'no caps, playing conditions adjustment or exceptional score reduction', which prevents over-trusting the result. It doesn't discuss error behaviour for missing/mismatched parameters, which keeps it below a 5.

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 domain scope, then inputs, then outputs, in one dense paragraph with no filler sentences. The long parenthetical allowance list makes sentences heavy, but every clause carries information an agent would otherwise have to guess.

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 6-parameter, all-optional, annotation-free tool with no output schema, the description covers inputs, both calculation modes, the return value ('the working and a link') and the accuracy caveats. The one gap is that all parameters are optional yet no guidance is given on what a call with no usable inputs returns.

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 baseline is 3, but the description genuinely adds meaning the schema lacks: the format-to-allowance mapping (95% singles stroke play/stableford, 85% fourball better ball, 100% singles matchplay, 90% fourball matchplay, 50% foursomes) and the course-handicap formula tying index, slope, rating and par together. The 'most recent first' ordering is repeated from the schema but the rest is additive.

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 domain (World Handicap System), the exact resources computed (handicap index, course handicap, playing handicap) and the region scoped (UK golfers). Its purpose is unmistakable against the all-price/search sibling set, none of which overlap.

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 two input modes are clearly spelled out: score_differentials for an index, and/or handicap_index with slope_rating, course_rating and par for course/playing handicap. There are no sibling alternatives to route between, so no exclusion guidance is needed, though it doesn't say what happens if neither mode's inputs are supplied.

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 does disclose meaningful behavior: the drop thresholds, the 7-day window, the data source (own price history, not retailer 'was' prices), and that qualifying items must still be at the lower price. It omits return shape, ordering, and whether results are capped or paginated.

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, both doing work, with the subject (price drops in the last 7 days) front-loaded ahead of the qualifying thresholds. Slightly dense because the criteria are packed into the second sentence, but 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 single-parameter, read-only listing tool with no output schema and no annotations, the description covers what the results represent and the strictness of the filter. It leaves the return fields and pagination/limit behavior unexplained, which is a moderate but not severe gap at this complexity level.

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?

One parameter ('limit') with 100% schema description coverage, so the schema already explains it as 'Max results, default 15'. The description adds nothing about the limit or how it interacts with the result set, which is the expected baseline when the schema does the documentation work.

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 with scope: 'price drops in the last 7 days on golf models GolfPrice AI tracks'. It also defines the qualifying criteria (≥£5 and 3%, still at the lower price), which makes the tool's domain unambiguous. It does not explicitly contrast itself with get_golf_price or search_golf_prices, so sibling differentiation is left 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?

The description defines the filter semantics ('genuine' drops, measured against GolfPrice AI's own history, 'never a retailer's was price'), which implicitly tells the agent this is a curated-deals view rather than a general lookup. However, it never states when to choose this over get_golf_price or search_golf_prices, nor any exclusions or prerequisites.

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. It does disclose that it returns 'short, practical answers' with a link to the full guide, but it does not state whether answers are generated or static, nor does it describe limitations, accuracy caveats, or response structure beyond the link.

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 purpose is front-loaded, but the description is one long sentence that includes a lengthy topic list and a promotional tangent about Society HQ running from one WhatsApp link. The core call information is present, but some details do not earn their place in a tool definition.

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 informational Q&A tool with 100% schema description coverage and no output schema, the description is largely complete. It conveys the topic scope, the short-answer-plus-link behavior, and the organiser audience, though it could clarify response limits or whether arbitrary questions are fully supported.

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 topic parameter is fully documented in the schema with allowed values and the fallback 'Or a question in words.' The description lists the same topics but adds no meaningful syntax or semantics beyond what the schema already provides, so the high schema coverage makes a baseline 3 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 that this is a guide/Q&A tool for golf society organisers, returning short practical answers plus a link to a full guide. It lists specific topics (run, awards, prizes, cost, handicaps, rules, apps, organiser), but it does not explicitly distinguish itself from the sibling golf_society_prize_ideas tool, which also covers prize ideas.

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 by the topic list and the phrase 'for golf society organisers', suggesting it should be used for golf society questions. However, there is no explicit when-to-use, when-not-to-use, or routing guidance among siblings, especially the overlapping golf_society_prize_ideas tool.

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 it does well: it discloses the default 35% prize share, the default split percentages, the re-weighting rule when side prizes are off or 4th place is paid, and rounding to 50p. What is missing is explicit confirmation that the tool is a pure calculation with no side effects or persistence, though that is strongly implied.

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 purpose is front-loaded, followed by the formula, then defaults and re-weighting rules. It is a single dense sentence, and every clause carries information an agent needs; the density is the only minor cost to readability.

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 6-parameter calculator with no annotations and no output schema, the description explains the computation, defaults, interactions, and rounding well enough to call it correctly. It does not spell out the exact response shape (e.g., per-position amounts), but the split rules make the output structure largely inferable.

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 baseline is 3. The description goes beyond the per-field schema by explaining how parameters interact: the percentage applies to entry money, the split percentages are re-weighted based on the boolean flags, and results are rounded. This is meaningful added semantics despite the schema already documenting each 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 opens with a specific verb and resource: working out a golf society day's prize fund and how to split it. It is clearly distinct from siblings like golf_society_prize_ideas (ideas, not arithmetic) and best_golf_under_budget, so an agent can select it without opening the schema.

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 by the arithmetic nature of the tool, and the mention of 30–40% as the typical range gives practical framing. However, there is no explicit statement of when to use it versus golf_society_prize_ideas or golf_society_guide, and no exclusions or prerequisites are named.

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 discloses the data source (GolfPrice AI), refresh cadence (checked every 6 hours), multi-retailer UK coverage, that prices are 'lowest new UK' and currency is GBP. It omits auth/rate-limit or failure behavior, but for a read-style lookup this is substantial disclosure.

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 exact query phrasings an agent would match, then the result semantics, then a pointer to a complementary resource. Dense but mostly earns its words; the final Prize Picker sentence is mildly promotional but adds a genuine alternative.

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 must convey return values — it does (real golf prizes, brand-mixed, priced within budget, sourced from UK retailers). Combined with full schema coverage on inputs, an agent has enough to call it correctly; only concrete result-shape details (count, fields) are absent.

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 all four parameters; baseline 3 applies. The description adds modest value by framing max_price as 'budget per prize' in GBP and implying min_price shapes the idea spread, but it does not add syntax or defaults beyond the schema's own notes.

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: retrieves real golf prizes whose lowest new UK price is within budget, mixed across brands. Concrete example categories (balls, gloves, bag bits, clothing) make the output shape clear and distinguish it from a generic price search.

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?

Opens with three example user questions that map directly to this tool ("prize ideas under £20", "what to buy the winner", "wooden spoon prize"), giving strong when-to-use signals. It does not explicitly contrast with siblings like best_golf_under_budget, so differentiation is left 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 and does well: it discloses the payload contents, that prices are today's lowest UK price, and crucially the data provenance caveat — popularity among GolfPrice AI visitors who took part, not a sales chart. It stops short of saying whether figures are live, cached, or how often the snapshot changes.

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 core concept, then the eight-slot enumeration, then trigger examples, then the caveat — a sensible order. The first sentence is long and parenthetical-heavy, but no clause is 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?

There is no output schema, yet the description enumerates the return shape (top pick per slot, pick share, lowest UK price, total), so an agent knows what to expect. For a zero-parameter read tool with a stated provenance caveat, nothing needed for correct invocation 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?

Zero parameters, so there is nothing to disambiguate and the baseline of 4 applies. The description correctly signals a no-argument aggregate query, so an agent will not hunt for filters.

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 resource (a popularity-weighted 'bag' across eight named slots) and exactly what each slot returns: top pick, pick share, and today's lowest UK price plus a total. The example questions ('what driver do most golfers use?') pin the scope precisely and separate it from price-comparison or kit-building 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?

Three concrete example questions give a clear trigger for when to call this rather than look up single prices. It does not name any alternative sibling (e.g. start_golf_kit or best_golf_under_budget) or state when not to use it, so routing is implied rather than spelled out.

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 discloses data freshness ('checked every 6 hours'), an explicit exclusion ('second-hand stock is excluded'), and the shape of the result (lowest new in-stock UK price today, retailer count, buy/wait verdict, link to history). It omits auth/permission needs and any rate-limit or pagination behavior, which keeps it short of a 5.

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 sentences, front-loaded with the action and scope, then return values, then freshness and exclusion caveats. No filler, no repetition of the title, and each clause adds information an agent needs.

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 compensates well by enumerating what comes back and how fresh the data is. It is slightly incomplete on the meaning of the 'buy/wait verdict' and on result-volume behavior beyond the schema's limit field, but nothing essential for a correct call 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 the schema already documents all four parameters, including query and category examples. The description's examples ('Pro V1', 'TaylorMade driver', 'rangefinder under 200') largely restate the schema and only mildly illustrate how query interacts with max_price, so 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?

States a specific verb and resource ('Search ... live UK golf price tracking by model, brand or category') with concrete query examples, and the plural framing ('each matching model's lowest ... price') functionally separates it from the single-price get_golf_price and the drop-oriented golf_price_drops. An agent can tell what this tool does without opening the schema.

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 useful input-side guidance (what can be searched, example queries, a price-filter example) and scope limits (UK, new stock only), but never says when to use this versus get_golf_price or golf_price_drops. Routing among the three siblings is left entirely to inference.

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: it discloses price freshness (checked every 6 hours), multi-retailer sourcing, stock constraint (men's sizes 8–10), the shape of each item's return (price and link) plus a total, and a historical price from about a month ago. It omits currency confirmation (implied GBP by 'UK prices') and any failure/empty-stock behavior.

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?

Purpose and price-freshness are front-loaded and the example questions close it out, so it is well ordered. The core is one very long, dense sentence with heavy parenthetical stacking, which is slightly harder to parse than it needs to be.

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 returns — and it does (per-item price and link, total, month-ago cost). With one optional parameter fully explained, an agent has everything needed to call it correctly; only currency and out-of-stock handling are left implicit.

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% and the schema's enum note is terse ('trying (default) or hooked'), but the description explains what each level materially means — 'trying' is the cheapest sensible kit, 'hooked' is a full 13/14-piece set with better balls. That is genuine semantic value beyond the structured field.

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 resource — a curated beginner's golf kit priced across UK retailers — and enumerates the contents (package set with bag, shoes, dozen balls, glove, tees) plus the total. This is clearly distinguishable in substance from the generic pricing siblings (search_golf_prices, compare_golf_prices), though it never names them explicitly.

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 concrete trigger questions ('how much does it cost to start golf?', 'what golf kit does a beginner need?') that map cleanly to the tool's scope. It stops short of stating when NOT to use it or pointing to the cheaper-granularity siblings like get_golf_price.

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