SnowSure — Snow & Ski

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

Annotations indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds context: text-only, ~1s response, does not render UI cards, grounded in SnowSure data. No contradictions.

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 paragraph but front-loads core purpose and lists specifics. While not overly verbose, it could be slightly more concise. Every sentence adds value.

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

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

The description covers purpose, usage, behavior, and exclusions. With 6 parameters and an output schema (not shown but exists), it adequately frames the tool. No gaps identified.

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% (all 6 parameters described). The description adds usage context for parameters like resortSlug and hemisphere, explaining how they scope the answer. This goes beyond the schema descriptions.

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 defines the tool as a text-only Q&A grounded in SnowSure data, listing specific use cases like head-to-head comparisons and season-opening norms. It distinguishes from siblings by explicitly stating what it should not be used for (photos, guide cards).

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 guidance on when to use (open-ended questions, comparisons, factual trivia) and when not to use, with specific alternative tool names (get_resort_photos, get_resort_info). Includes concrete examples, enabling correct selection.

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?

Despite annotations providing idempotentHint and openWorldHint, the description adds critical behavioral context: 'we never charge', 'we hold the rate + attribute the booking', 'payment always completes on LUXSKI'. It also notes OAuth requirement, going beyond annotations.

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 at 4 sentences, front-loading the purpose. It could be slightly more structured (e.g., bullet points) but is efficient and clear.

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 complexity (two modes, auth, external payment), the description covers all major aspects: what it does, prerequisites, and side effects. Output schema exists, so return values need not be explained.

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% (baseline 3). The description adds meaning by explaining that hotelName+dates trigger prebooking and checkout URL, and that powderEventId is optional for attribution. This enhances understanding beyond schema descriptions.

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 finds lodging near a resort via LUXSKI for the signed-in user. It distinguishes two modes: with hotelName+dates prebooks and returns a checkout URL, without returns availability+booking link. The verb 'find' and 'prebook' are specific and differentiate from siblings like find_flights_to_powder.

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 explains when to use which mode (with vs without hotelName) and requires a SnowSure user access token. However, it does not explicitly state when not to use the tool or mention alternatives, though it is clear in 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?

Annotations already show readOnlyHint and idempotentHint, but description adds that it shows model agreement and uncertainty, which are behavioral details beyond annotations.

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 concise sentences with front-loaded purpose and clear usage. Every word adds value.

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

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

Given output schema exists, description fully covers purpose, models, and output nature. No missing behavioral info.

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 'slug' with schema description 'Resort slug to compare forecasts for'. Description adds no extra meaning beyond schema; baseline 3 due to 100% schema 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?

Description clearly states verb 'compare', resource '14-day snow forecasts across 7 weather models', and purpose 'forecast reliability queries'. Distinguishes from sibling tools like get_weather_forecast (single model).

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 says 'Use for forecast reliability queries.' Implicitly distinguishes from single-model tools but lacks explicit when-not-to-use or direct 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?

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, which the description aligns with by describing a comparison operation. The description adds behavioral context (e.g., 'which pass covers them and whether it beats buying window lift tickets') beyond annotations, though no detailed edge cases are mentioned.

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, each earning its place: first states purpose with an example, second provides optional parameter usage. Front-loaded with the most critical information, 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 tool with 4 optional parameters and an existing output schema, the description adequately explains the core functionality, usage scenario, and how inputs affect outputs. No gaps identified.

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 baseline is 3. The description adds value by showing how parameters interact in a meaningful example (e.g., passing resortSlugs and tripDays to see coverage and value), which clarifies the intended use beyond the schema's individual descriptions.

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 compares ski passes on price, resort coverage, and value. It gives a concrete example ('which season pass includes <resort> — Epic or Ikon?'), distinguishing it from sibling tools like compare_resorts or find_pass_resorts.

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: 'the tool for...' and instructs to optionally pass resortSlugs and tripDays to get coverage and value analysis. It lacks explicit when-not-to-use alternatives, but the example strongly implies its primary use case.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the agent knows it's safe and idempotent. Description adds value by stating data provenance 'every value comes from the SnowSure conditions resolver/contract', providing transparency beyond annotations.

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, front-loaded with main purpose, followed by optional detail. Every sentence adds value with 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?

Given 2 parameters (one required), output schema present, and full schema coverage, the description covers purpose, data source, and optional restriction. No additional context needed.

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 descriptions for both parameters. Description adds context by specifying that comparison is across snow/terrain/live conditions and that dimensions parameter restricts to a subset listed in the schema. This adds meaning beyond the schema alone.

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 clearly states the verb 'compare', the resource 'resorts', and the scope '2–4 resorts side by side across snow, terrain, and live conditions'. It distinguishes from sibling tools like compare_forecasts and compare_passes by specifying the categories and data source.

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 mentions optional restriction to specific dimensions, implying when to use the optional parameter. Implicitly distinguishes from siblings by focusing on snow/terrain/live conditions from SnowSure, but lacks explicit 'when not to use' or direct comparison to alternative tools.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds that it returns results sorted by 24-hour snowfall. No contradictions; the description enhances understanding beyond annotations.

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: first states action and sorting, second gives use cases. No wasted words, front-loaded with key information.

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 output schema present, description need not detail returns. It covers core functionality and use cases. Minor omission: does not mention optional nature of parameters, but schema covers that.

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 parameters (limit, region, minSnowfall) are well-documented. The description does not add extra semantics 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 clearly states it finds resorts with the freshest powder snow, sorted by 24-hour snowfall. This distinguishes it from sibling tools like get_snow_report (single resort) and find_resorts_by_criteria (general 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?

Explicitly recommends use for 'where is it snowing?' or 'fresh powder' queries. However, it does not mention when not to use or contrast with alternatives like get_weather_forecast or get_snow_history.

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?

Description adds value beyond annotations by stating it returns flight-search links (not actual bookings) and uses resort slug with optional origin. Annotations already indicate read-only and idempotent, and description aligns without contradiction.

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?

Extremely concise: three sentences, front-loaded with main action, then pairing context, then argument details. 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?

Despite having an output schema, the description provides complete context: what the tool does, how to use, argument details, and integration with workflow. No 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%, and description enriches both parameters: explains resort slug requires using search_resorts, and origin can be omitted for origin-less search. Adds practical guidance beyond schema descriptions.

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 explicitly states it finds nearest gateway airports and provides flight-search links, using specific verbs and resource. It distinguishes from siblings by mentioning pairing with find_best_powder/find_powder_trips and book_lodging.

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 states when to use: 'Complete the trip' and 'the get there leg of the funnel'. Provides explicit context for usage, including pairing with other tools, and advises using search_resorts first for resolution.

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?

The annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the agent knows this is a safe, read-only operation. The description adds that the tool returns names and SnowSure slugs, which is useful output context. With strong annotations, the description need not repeat safety info, and it adds value beyond the structured fields.

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 only two sentences, yet it conveys the main purpose, example questions, output details, and chaining hint. Every sentence earns its place, and there is no fluff. The structure is front-loaded with the core action.

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 only 2 parameters, no enums, and an output schema (not shown but present), the description covers everything: what it does, when to use it, what it returns, and how to chain it. It is fully informative without being verbose.

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?

Input schema coverage is 100%, with both parameters having descriptions. The description adds semantics by explaining that the passId is like 'ikon-pass' and that region is an optional filter that matches the pass's region labels. This provides context beyond the schema's basic descriptions, such as example values and usage patterns.

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 verbs ('List', 'answers') and clearly identifies the resource ('resorts on a ski pass'). It provides example questions that agents might ask, making the purpose unmistakable. It distinguishes from sibling tools like get_pass (which gives pass details) and compare_passes (which compares passes) by focusing on listing resorts on a single pass.

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

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

The description provides explicit use cases ('is <resort> on the Ikon Pass?' and 'what resorts does the Epic Pass include') which tells the agent when to invoke this tool. It also hints at chaining by noting that it returns SnowSure slugs that can be passed to get_resort. However, it does not explicitly state when NOT to use it or compare with siblings, which would push it to 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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds booking flow details (links to LUXSKI) and reliance on forecast data, no contradictions.

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?

Description is moderately long but well-structured, front-loading purpose and examples. Each sentence adds value.

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

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

With output schema present, description doesn't need to detail return values. Covers purpose, parameters, usage, and external booking, making it fully adequate for the tool's 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?

Schema covers all 3 params (100% coverage). Description adds context for vibe filter ('handpicked hotels in that tier') and default limit, enhancing semantic understanding.

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 ranks resorts by 14-day powder forecast and returns handpicked luxury hotels, differentiating it from siblings like find_best_powder or plan_ski_trip by focusing on bookable trips with accommodation.

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 example queries ('where should I book for powder', 'best powder trip this month') and lists optional vibe filter. Lacks explicit when-not-to-use or alternatives, but usage 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?

Annotations already declare readOnlyHint, destructiveHint, and idempotentHint. The description adds context about filtering criteria (snow depth, elevation, etc.) but does not disclose additional behavioral traits beyond what annotations provide.

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, front-loads the purpose, and contains no wasted words. Every sentence adds value.

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

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

Given that output schema exists and annotations cover safety, the description is largely sufficient for a filter tool. It could mention that all parameters are optional, but schema already indicates no required parameters.

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 baseline is 3. The description lists some parameter examples from the schema but does not add new meaning or explain parameter interactions or format.

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 finds resorts matching criteria like snow depth, elevation, runs, and SnowSure rating. It distinguishes this from sibling tools like get_resort (single resort) and search_resorts (likely broader search) by emphasizing 'advanced filtering for trip planning.'

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 trip planning with filters but does not explicitly state when to use it versus alternatives like search_resorts or find_pass_resorts. There is no mention of exclusions or specific contexts.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds that the bulletin is relayed from official warning service with issuer and link, and specifies behavior when no bulletin exists. No contradictions with annotations.

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: first states purpose, second covers edge cases and warnings. No unnecessary words, front-loaded with key action and scope.

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?

Description is complete for a read-only tool with output schema present. Covers geographic scope, behavior for missing data, source attribution, and important caveat about training. No missing context.

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 'slug' described adequately. Description does not add further parameter details beyond what schema provides, meeting the baseline for high 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?

Description clearly states verb 'get' and resource 'avalanche danger bulletin' with geographic scope (US, Canada, Switzerland). It distinguishes from sibling tools like get_snow_report or get_weather_forecast by focusing specifically on official avalanche bulletin.

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 context for when to expect 'no bulletin' (off-season, no forecast service) and includes a caveat about not being a substitute for official bulletin or training. However, it does not explicitly compare to sibling tools or state when NOT to use it versus 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?

Annotations already provide readOnlyHint=true and idempotentHint=true; the description adds that it returns 'a table of member ski areas', giving behavioral context beyond the schema/annotations. No contradictions.

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 zero waste. The first sentence states purpose and examples; the second provides usage guidelines with alternatives. Every sentence 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?

The tool has an output schema, so return values need not be explained. The description covers purpose, usage constraints, and sibling differentiation. Complete for a read-only list tool.

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% (both slug and limit have descriptions). The description does not add extra 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 starts with 'Multi-mountain destination hub' and explicitly lists example hubs, clearly stating the tool's verb (get) and resource (destination hub). It distinguishes from siblings 'get_resort_photos' and 'get_resort_info' by naming exact conditions.

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 tells exactly when to use: 'Use ONLY when the user names the hub itself'. It provides explicit when-not and alternatives: 'NOT for photo gallery (→ get_resort_photos on a specific mountain slug) and NOT for resort guide cards (→ get_resort_info)'.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context by clarifying that insightType=intelligence skips simple leaderboards, which is useful beyond the annotations. No contradictions.

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, no filler. The first sentence states the purpose and examples; the second gives filtering guidance. Every word earns its place, and it 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 4 optional parameters (all covered in schema), existing output schema, and clear annotations, the description fully covers what the tool does, how to use it, and key behaviors. It even includes a prerequisite step (calling list_insight_categories). No 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 enums and descriptions for all 4 parameters. The description adds the workflow hint (call list_insight_categories first) and the distinction between data and intelligence types, which enhances understanding 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 clearly states it retrieves categorized SnowSure intelligence insights, not just snow totals. It gives concrete example questions (season vs 5yr norm, powder leaders, model accuracy) and distinguishes from simple leaderboards via insightType. This differentiates it from siblings like get_snow_report or get_ml_trends.

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

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

The description provides explicit usage context: answers specific insight questions, recommends filtering by category or insightType=intelligence, and instructs to call list_insight_categories first. However, it does not explicitly mention when to avoid using this tool or contrast with sibling alternatives, which would improve the score.

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?

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, which are consistent. Description adds context like 'public REST API' and 'SnowSure-unique', but does not contradict annotations.

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: first states purpose and scope, second provides usage guidance and alternative. Front-loaded, every sentence 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?

Given complexity (10 params, 13 dataset enums), description is adequate for initial use. Schema covers all parameters and output schema likely explains return values. Could be more detailed on each dataset, but sufficient for selection.

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%, baseline 3. Description adds guidance like 'Start with dataset=catalog' and explains parameter dependencies (e.g., 'slug required for score_components'). Adds meaningful context beyond 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?

Description clearly states it fetches 'SnowSure-unique ML/AI trend datasets from the public REST API' and lists specific datasets. It distinguishes from sibling 'get_insights' by specifying use for raw rankings vs. narrative cards.

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 advises to 'Start with dataset=catalog' and to prefer 'get_insights for narrative intelligence cards' while using this for 'raw rankings and time series'. Provides clear when-to-use and when-not-to-use.

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?

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds context: uses ~30 years of ERA5 data, months with too little history are withheld, and each month carries yearsTracked. No contradiction.

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?

Description is slightly long but well-organized and front-loaded with key purpose. Every sentence adds value, with clear examples and usage guidance.

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 output schema exists and only one parameter, the description covers purpose, usage, and behavioral nuances thoroughly. No gaps for the 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?

Only one parameter (slug) with full schema coverage (100%). Description does not add specific parameter semantics beyond the schema, but provides overall context for the tool. 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 provides typical snow history from ERA5 reanalysis, listing metrics like average snowfall, snow days, depths, and biggest storm. It distinguishes from siblings by naming get_weather_forecast and get_resort as 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?

Explicitly says to use for date-choosing questions (e.g., 'what is February usually like at Vail', 'when should I go') and clarifies it's not a forecast or prediction, pointing to specific 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?

Annotations already provide readOnlyHint and other safety hints. The description adds that the tool requires OAuth authentication and ranks resorts best-first with freshest snow on top, providing behavioral context beyond the annotations.

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, front-loaded with the core purpose, and includes the authentication requirement. Every sentence adds value, and there is 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?

Despite zero parameters, the description fully covers what the tool does, for whom, the required authentication, and the ranking logic. The presence of an output schema means return values are covered elsewhere, so completeness is excellent.

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?

There are 0 parameters, so the baseline is 4. The description does not need to explain parameters, and no schema descriptions are required.

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 provides a personalized snow report for the signed-in user's saved resorts, including live conditions and ranking. It distinguishes itself from siblings like get_snow_report (general) by specifying personalization and user-specific context.

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 mentions the requirement for a SnowSure user access token (OAuth), which is a key usage condition. The personalization aspect implies it should be used when the user wants their own saved resorts, but it does not explicitly compare with alternatives or state when not to use.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds valuable behavioral context: modeled guidance, not official, caveat to check live status. No contradictions.

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 perfectly front-loaded: first states purpose with key details, second adds critical caveat. 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?

Tool has output schema, and description covers purpose, time horizon, factors, and limitations. Complete for a risk-estimation tool among many siblings.

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 'slug' with schema description 'Resort slug'. Schema coverage is 100%, so description adds no extra meaning. 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?

Description clearly states the tool provides a 48-hour operating-risk estimate for lifts, listing specific factors (wind-hold, visibility, cold, heavy-snow). It distinguishes from both the resort's own decision and sibling tools like get_weather_forecast and get_snow_report.

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 states 'Modeled guidance, NOT the resort's own operating decision — always check live lift status.' This tells the agent when to use (risk estimate) and when not to rely solely on it, effectively guiding usage and 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?

Annotations already declare readOnly, idempotent, non-destructive. The description adds concrete return fields (operator, pricing, regions, buy link), which enriches behavioral understanding. No contradictions with annotations.

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 one efficient sentence, front-loading purpose and listing key outputs. It is concise but could be slightly shortened by removing the ellipsis or examples.

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 tool is simple (1 param, output schema exists). The description covers the return content adequately. No discussion of errors or special conditions, but annotations and schema fill most gaps. Slightly better examples could help.

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 a clear description for the single parameter 'passId'. The tool description does not add extra semantic value beyond the schema, so baseline score of 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 clearly states the tool returns details for a multi-resort ski pass, listing specific fields (operator, pricing, regions, link). It distinguishes from siblings like 'compare_passes' and 'find_pass_resorts' by focusing on a single pass's details.

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 getting pass details but does not explicitly state when to use this tool over alternatives like 'compare_passes' or 'find_pass_resorts'. No exclusions or alternatives 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, indicating a safe read operation. The description adds behavioral context beyond annotations: it mentions that accumulation data is burned into frames ('Proof of Powder'), that results are returned newest-first, and that watch links are provided. No contradictions with annotations.

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 paragraph that front-loads the tool's core purpose, then lists example queries, and ends with the return behavior. It is efficient but could be slightly more structured with bullet points. Every sentence adds value.

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

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

Given that an output schema exists (not shown but acknowledged), the description does not need to explain return values. It covers filtering options (resort, date), ordering, the nature of the data (verified accumulation), and typical use cases. This is sufficient for an agent to select and invoke the tool 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% with all three parameters (date, limit, resort) having descriptions. The description does not add significant new semantics beyond the schema; it restates that resort is optional and date requires resort (already in schema). The use-case examples indirectly guide parameter usage but do not provide deeper meaning. Baseline 3 is appropriate given high schema 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 the tool retrieves Powder Reels — archived storm timelapses with verified accumulation data. It provides specific example queries like 'how much did it snow at X last night' and mentions the output ordering (newest-first with watch links). This distinguishes it from sibling tools focused on forecasts, webcams, or snow reports.

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 lists use cases for storm footage, timelapses, and powder receipts. While it does not explicitly state when not to use this tool, the context makes it clear it is for historical, verified storm recaps rather than live conditions or forecasts. The sibling tools like get_webcam_status and get_snow_report provide alternatives, but the description does not directly call them 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is clear. The description adds that the output includes statistics and top resorts, complementing the annotations without contradiction.

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

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

The description is a single, well-structured sentence of 18 words. It front-loads the action and scope, efficiently conveying purpose and output content 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?

Given the tool's simplicity (two optional params, but at least one required) and the presence of an output schema, the description adequately covers the core functionality. It could explicitly mention that region or country is required, but the schema provides that detail.

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 descriptions for both parameters, so the baseline is 3. The main description only loosely refers to region/country without adding specifics beyond what the schema already provides.

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 provides a summary of snow conditions across an entire region or country, including statistics and top resorts. This distinguishes it from sibling tools like get_snow_report (single resort) or compare_resorts (specific comparisons).

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 use for broad geographical summaries, and the schema emphasizes providing at least a region or country with preference for region on multi-country areas. However, no explicit alternatives or when-not-to-use guidance is given, though 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. Description adds behavioral context: the tool returns an 'interactive UI' based on card parameter, and details content of each card. No contradictions.

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?

Description is somewhat lengthy but necessary to cover all four card types and usage mappings. Front-loads the key point about the required card parameter. Could be slightly more structured (e.g., bullet list) but still effective.

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 complexity (4 card options, 2 parameters, output schema exists), the description fully covers usage: explains each card's content, gives request mapping, and provides alternatives. No 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 provides descriptions for both parameters (100% coverage). Description adds significant value: natural language mapping for the card parameter (e.g., 'Resort guide' → card=guide) and example slug values. This enriches the schema 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 states it provides 'Single-resort data' and explains each card type (guide, photos, snow, full). It explicitly distinguishes from sibling tools like get_resort_info and get_resort_photos, which are mentioned as 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?

Explicit instructions: 'REQUIRED card parameter', mappings from user requests to card values, and a preference note to use get_resort_info/get_resort_photos when available. This provides clear when-to-use and when-not-to-use 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds no additional behavioral traits beyond what is implied (read-only retrieval). No contradiction, but no added value.

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?

Description is front-loaded with key content list in ALL CAPS, provides examples and usage conditions. Slightly verbose but still efficient and structured.

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?

Fully covers purpose, usage, parameter, and excludes return values thanks to output schema existence. No gaps for a tool with one required parameter.

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%, with the 'slug' parameter well-described in the schema (examples given). The description does not add further semantic detail beyond the schema, so 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?

Description clearly states it's an interactive resort guide card with specific content (elevation, vertical, lifts, runs, etc.) and distinguishes from sibling 'get_resort' which shows snow conditions. The verb+resource is explicit and detailed.

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 lists when to use: resort guide, mountain profile, resort info, lifts/runs/vertical/skiable area, season dates, ski passes, or 'tell me about the mountain' (non-weather). Provides examples and warns against using 'get_resort' for this purpose.

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?

Annotations already declare safe read-only behavior. The description adds useful context about the output format (interactive carousel, official SnowSure photos) but doesn't contradict annotations. No additional behavioral traits needed beyond what's provided.

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?

Extremely concise with no wasted words. Key information is front-loaded with capitalization for emphasis, and every sentence adds value.

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

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

Given the simple single-parameter tool with output schema and annotations, the description is fully complete. It covers purpose, usage, and exclusions without missing any essential context.

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 description adds minimal value beyond the schema's parameter definition. It provides example slugs but no extra semantic meaning.

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: retrieving interactive photo galleries for resorts, using specific verbs like 'get photos' and resources. It distinguishes from siblings by explicitly forbidding use of web search or other tools for photo requests.

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 when-to-use and when-not-to-use guidance, listing trigger keywords (photos, pictures, images, gallery) and naming sibling tools to avoid (get_destination, get_resort_info, ask_snowdata).

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?

Annotations already indicate readOnly and idempotent. The description adds that the tool links to official DOT maps and notes that the resort's road status is authoritative, clarifying data sources. It also states the fallback return value, which is helpful context beyond annotations.

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 that efficiently pack purpose, scope, examples, coverage limitations, and a link to authoritative sources with 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?

The description covers geographic scope, return behavior for uncovered areas, and a reference to official maps. With an output schema present (not shown), the description provides sufficient context for an agent to understand what the tool returns and its limitations.

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 'slug' exists with schema description 'Resort slug'. The tool description does not add further detail about this parameter, but schema coverage is 100%, so the baseline score 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 clearly states the tool provides driving access info for resorts, answering specific questions about chain requirements and road conditions. It lists covered states (California, Washington, Colorado, Utah) and the type of data returned (chain-control, mountain-pass, road-surface).

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 specifies what the tool does and its geographic scope, noting that outside coverage it returns 'no road data'. However, it does not explicitly contrast with sibling tools like get_road_cameras or get_road_weather, nor does it advise when not to use it.

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?

Annotations already indicate safe, read-only behavior (readOnlyHint, destructiveHint). The description adds valuable behavioral context: coverage limited to specific states, returns 'no road cameras' for areas outside coverage, and each camera includes a live image URL. No contradictions.

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

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

The description is a single, well-structured sentence that front-loads the core action and key details. Every piece of information is relevant and no words are wasted.

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 tool with one parameter and existing output schema, the description covers all essential aspects: purpose, regional coverage, edge case handling, and output content. No 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?

The parameter 'slug' is fully described in the schema with 100% coverage. The description does not add further detail about the slug parameter's format or expected values, which is adequate given schema 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 specifies the tool's purpose: retrieving live roadside camera stills near a resort from specific DOT/CCTV sources (Caltrans, WSDOT, UDOT). It explicitly distinguishes from resort webcams and explains the output behavior, making it clear and distinct from sibling 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?

The description notes that it is 'distinct from resort webcams,' providing some guidance on when not to use this tool. However, it does not explicitly list alternative tools for similar queries (e.g., get_webcam_status), leaving some ambiguity.

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?

Annotations already declare readOnly, idempotent, and non-destructive nature. The description adds valuable context: data sources (CDOT, WSDOT, UDOT), that the data is measured sensor data (not modeled), and the edge case behavior ('returns 'no road weather' outside covered area'). No contradiction with annotations.

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 plus a phrase, efficiently conveying purpose, data points, geographic scope, source distinction, and edge case. No unnecessary words, 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 the existence of an output schema to document return values, the description covers the tool's purpose, data types, geographic coverage, distinction from a sibling tool, and edge case behavior. It lacks explicit prerequisites or limitations (e.g., that the resort slug must correspond to a valid resort in one of the three states), but overall it is fairly 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 coverage is 100% with one parameter (slug) described as 'Resort slug'. The description does not add further details about the parameter itself but provides overall context about the data returned, which helps interpret the parameter's role. 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 specifies the tool measures roadside weather (RWIS) with detailed data points (surface+air temperature, visibility, wind, precipitation) and lists specific state data sources. It distinguishes itself from the modeled get_operating_risk tool, providing clear differentiation.

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 for when to use this tool ('sensor data on the actual road') and explicitly contrasts with get_operating_risk ('distinct from the modeled'). However, it does not mention alternatives among other road-related sibling tools like get_road_access or get_road_cameras.

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?

Discloses that it returns only scheduled/confirmed openings on that specific date, NOT all currently-open resorts. This adds context beyond the readOnlyHint and idempotentHint annotations.

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?

Concise 4-sentence description: purpose, examples, usage guidance, and clear structure. Every sentence adds value; no fluff.

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 low complexity (4 optional params, output schema exists), the description is complete: covers purpose, usage, behavioral constraints, and distinctions from siblings.

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 covers all 4 parameters (100% coverage). Description provides examples of usage but does not add new semantic detail beyond the schema. 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?

Clearly states the tool returns resorts whose season opening date is a specific day, with concrete examples. Distinguishes itself from sibling tools like get_southern_hemisphere_report and ask_snowdata.

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 advises when to use: for opening-day questions. Also tells when not to use: for historical-norm questions (use ask_snowdata). Clearly prefers over get_southern_hemisphere_report for opening-day 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?

Annotations already declare readOnlyHint=true and idempotentHint=true. Description adds no behavioral context beyond what annotations provide, so baseline 3 is appropriate.

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?

Single sentence, front-loaded with verb and resource, no wasted words. Every part adds value.

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

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

With output schema present, return values need not be explained. Description mentions key data points. Could mention prerequisite of having a resort slug from a related tool, but overall adequate.

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 one parameter 'slug' described as 'Resort slug'. Description adds no additional 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?

Description clearly states verb 'Get' and resource 'historical snowfall data', listing specific data included (season totals, comparisons, best months). This distinguishes from siblings like 'get_monthly_snow' or 'get_snow_report'.

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?

Description implies use for historical snowfall data but does not explicitly state when to use this tool over alternatives, nor 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?

Annotations already declare readOnlyHint and idempotentHint, indicating safe read behavior. The description adds no new behavioral context beyond what annotations provide, which is adequate but not enhanced.

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 with no filler, front-loaded with the core purpose, and efficiently conveys use cases.

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 existence of a full output schema, the description doesn't need to explain return values. It covers the tool's primary function and query intent, but omits mention of region filtering and limit, which are in the schema. Still, the context is sufficient for most agents.

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 description need not repeat all parameter details. However, the description mentions only three sort options (snowsure, forecast, recent) while the schema includes 'depth', and it does not explain 'limit' or 'region' parameters, providing partial but incomplete semantics.

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 a global snow report with top-ranked resorts by snow conditions, providing specific use cases. However, it does not differentiate from similar sibling tools like 'get_my_snow_report' or 'find_best_powder'.

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

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

The description provides explicit example queries for when to use the tool, but lacks guidance on when not to use it or mention of alternatives among the many sibling tools.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that it prefers operator-verified data when available, which is extra behavioral context beyond the annotations.

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 moderately concise (4 sentences). Each sentence adds value, but it could be slightly tighter. The use of examples and clear guidance is good.

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

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

Given the tool has 3 parameters with enums and an output schema exists, the description covers the core purpose, usage constraints, and a behavioral insight. It does not repeat return values as output schema handles that, but it provides sufficient context for selection.

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 fully describes each parameter. The description does not add additional meaning beyond what is in the schema, so baseline score 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 clearly states it gets snow conditions for Southern Hemisphere ski resorts currently in season, and provides example queries that match common user intents. It distinguishes itself from sibling tool 'get_season_openings' by specifying not to use it for opening dates.

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 says when to use (June–October SH ski queries) and when not to use (for opening dates, use get_season_openings instead). This provides clear guidance for the agent.

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?

Annotations already indicate readOnlyHint and idempotentHint. Description adds that it returns a forecast for up to 14 days with specific weather data, which is consistent and informative beyond annotations.

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?

Single sentence, front-loaded with action, no extraneous words. Efficiently communicates purpose and scope.

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 forecast tool with full schema coverage and existing output schema and annotations, the description is complete enough for an agent to understand what it does without additional clarification.

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 fully describes parameters (slug, days). The description does not add meaning beyond what the schema provides; it mentions output content but not parameter details.

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 clearly states the tool retrieves a day-by-day weather forecast for a resort, including specific weather elements (temperature, snowfall, wind, conditions). It distinguishes from siblings by specifying 'forecast' for the next 14 days, unlike historical or report 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?

No explicit guidance on when to use this tool vs alternatives. The description does not mention when not to use or provide references to sibling tools like 'get_snow_report' or 'get_monthly_snow'.

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?

Annotations already indicate a safe, read-only, idempotent operation. The description adds behavioral context by stating it returns live links and status, and supports named-cam lookups. This goes beyond annotations but lacks details like caching behavior or response size.

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, front-loading purpose and examples with zero waste. Every part earns its place, making it highly efficient for an agent 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?

Given the annotations cover safety, the output schema exists (though not shown), and the tool has a single parameter, the description is sufficiently complete. It could mention return structure briefly, but the output schema fills that gap.

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 has 100% description coverage for its single parameter 'slug', which is described as 'Resort slug'. The description does not add further meaning or format constraints, so it adds no semantic value beyond the schema. 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 live webcam links and status for a resort to visually assess current on-mountain conditions, and includes example queries. It is specific about the resource (webcams) and purpose, effectively distinguishing it from sibling tools like get_snow_report or get_resort_photos.

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 by listing example queries it answers, so an agent can infer when to use it (when asked about webcams). However, it does not explicitly mention when not to use it or suggest alternatives, which limits guidance for selecting among sibling tools.

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?

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds the authentication requirement (OAuth token), which is additional behavioral context beyond annotations. No contradictions.

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

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

The description is a single sentence with two clear clauses, no extraneous text, and front-loads the main action.

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 tool with no parameters, annotations covering safety, and an output schema (present but not shown), the description is complete. It adds the crucial auth requirement, leaving no ambiguity.

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 tool has zero parameters, so schema coverage is 100%. The description does not need to add parameter info, and the baseline for 0 parameters is 4.

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 lists the signed-in user's alert subscriptions. The verb 'list' and resource 'alert subscriptions' are specific, and the sibling tools 'subscribe_alerts' and 'unsubscribe_alerts' are distinct for creation and deletion, so purpose is well-defined.

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 mentions an OAuth token requirement, but does not explicitly state when to use this tool versus alternatives. Usage context is implied by the name and siblings, but not articulated.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds behavioral context by noting the categories are 'retrospective and verification-backed cards' and 'lighter than raw leaderboards', offering insights beyond the annotations.

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, no wasted words, front-loaded with the main purpose, then usage context. Every sentence is informative.

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 zero-parameter tool with an output schema, the description is complete: it explains what is listed, when to use, and the nature of the data. No 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?

Input schema has zero parameters with 100% coverage. The description adds value by listing example categories, effectively providing context for the output without needing parameter details.

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 action ('List SnowSure insight categories') and resource, and distinguishes it from siblings by mentioning it is used before get_insights and is lighter than raw leaderboards.

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 says when to use ('Use before get_insights to choose a category filter') and contrasts with alternatives ('Lighter than raw leaderboards'), providing clear 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?

The description adds behavioral insight beyond annotations by mentioning the OAuth requirement and the authorization error behavior, which annotations alone do not cover.

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: the first states the purpose, the second adds critical auth context. No wasted words, and the most important 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?

With no parameters and an output schema available, the description covers the purpose, auth requirement, and error handling, which is complete for a simple list tool.

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?

There are no parameters, so schema coverage is 100%. The description does not need to add parameter details; the context about auth is extra and not parameter-related. Baseline 4 for zero parameters.

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 'List the signed-in user's saved resorts' with a specific verb and resource, and it distinguishes from sibling tools like save_resort and remove_saved_resort.

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 specifies the requirement for a SnowSure user access token and explains the error response when missing, providing clear context for when to use the 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?

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds significant behavioral context: nearest-neighbor routing, minimal backtracking, day allocation, driving leg estimation, and integration of live conditions and chain control. No contradiction with annotations.

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: first sentence provides a comprehensive functional summary, second lists arguments. It is front-loaded and efficient, though could be slightly more structured with bullet points for 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 the tool's complexity (multi-stop routing, allocation, estimation, live conditions) and the presence of an output schema, the description covers all key behaviors and limitations. It is complete enough for an agent to understand the tool's purpose and operation.

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 67%, so baseline is 3. The description restates parameters ('region (required), days, optional pass.') but does not add meaningful new semantics beyond what the schema already provides for the parameters.

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 plans a multi-stop ski road trip, detailing specific operations like picking top resorts, ordering into a drivable route, allocating days, estimating driving legs, and incorporating live conditions. This differentiates it from the sibling 'plan_ski_trip' which likely plans a simpler trip without road routing.

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 when a user wants a road trip with routing and conditions. However, it does not explicitly state when not to use this tool versus alternatives like 'plan_ski_trip' or when other trip planning tools are more appropriate.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, making the read-only nature clear. The description adds context about inputs (dates, preferences, conditions) and output (suggests best resorts), but does not reveal additional behavioral traits such as how conditions are evaluated or whether the tool accesses real-time data. It adds moderate value beyond annotations.

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, with the first sentence stating the core function and the second providing a slight elaboration. It is front-loaded and concise, though the second sentence is somewhat redundant with the first, making it slightly less 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?

Given the existence of an output schema and full input schema coverage, the description provides adequate high-level context. However, with many sibling tools, the description misses the opportunity to clarify the tool's unique value proposition (e.g., it recommends resorts based on conditions, but doesn't distinguish from find_powder_trips). It is complete enough for basic use but lacks depth for distinguishing among alternatives.

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 reiterates that the tool uses 'dates, preferences, and conditions,' which aligns with the schema parameters. However, it does not add meaning beyond the schema's own detailed parameter descriptions. The description is consistent but not enhancing.

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 the specific verb 'Get' and the resource 'ski trip recommendations', and mentions 'best resorts for a given time period.' It clearly states the tool's purpose of providing recommendations based on dates, preferences, and conditions. However, it does not explicitly differentiate from sibling tools like find_powder_trips or compare_resorts, which could 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 description offers no guidance on when to use this tool versus alternatives. It does not specify when not to use it, nor does it mention prerequisites or limitations. The tool's relationship to siblings like find_powder_trips or compare_resorts is unaddressed, leaving the agent to infer usage 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?

The description adds the authentication requirement beyond annotations, which already indicate destructiveness and idempotency. However, it does not disclose behavior for invalid slugs or non-existent resorts.

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 concise sentences front-load the purpose and follow with a necessary prerequisite, with no extraneous information.

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 delete operation with an output schema, the description covers purpose and auth. Slightly incomplete regarding error handling, but sufficient given the 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 input schema fully describes the parameter 'slug', and the description adds context that the resort slug refers to one in the user's saved list, providing marginal extra value 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 clearly states the action ('Remove a resort from the signed-in user's saved list') and distinguishes it from sibling tools like save_resort and list_saved_resorts by specifying the target is the user's own saved list.

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 mentions an authentication prerequisite ('requires a SnowSure user access token') but does not explicitly state when to use this tool versus alternatives or provide exclusions.

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?

Annotations specify idempotent and non-destructive; description adds context about token and slug validity. No contradictions. Could add detail on duplicate handling, but annotations already imply safety.

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 concise sentences with no irrelevant information. Purpose stated first.

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 tool with one parameter and output schema present, the description covers essential input and behavior. Could mention output structure if not obvious, but schema handles that.

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 parameter slug has a description and example in schema. The description adds validation requirement ('must be a real SnowSure resort'), enhancing schema 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 the action (save), resource (resort), and destination (user's favorites). It effectively distinguishes from sibling tools like remove_saved_resort and list_saved_resorts.

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?

Mentions prerequisites: requires OAuth token and real slug. Provides clear context for when to use the tool, though no explicit when-not-to-use 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?

The description states 'Returns matching resorts with basic conditions,' which adds minimal behavioral context beyond the annotations (readOnlyHint=true, destructiveHint=false). The annotations already convey safety, so the description is adequate but not rich in behavioral 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?

Two concise sentences: first states purpose, second provides usage guidance. No extraneous words. Information is front-loaded and 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?

Given the presence of an output schema and thorough annotations, the description covers the essential behavioral and usage aspects. The tool's purpose, parameters, and query patterns are clearly explained, leaving no gaps for an agent to misinterpret.

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?

All three parameters (limit, query, country) are fully described in the input schema (100% coverage). The description adds no new semantic information beyond what the schema already provides, such as default values or exact formats. Baseline score 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 uses a specific verb ('Search') and resource ('ski resorts') and provides example queries ('find resorts in [location]' or 'search [name]'), which clearly distinguishes it from sibling tools like compare_resorts, get_resort, or find_resorts_by_criteria.

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 explicit usage patterns ('Use for...') which helps the agent know when to invoke this tool. However, it does not mention when not to use it or compare to alternatives like get_resort for detailed info, leaving some ambiguity.

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?

Auth requirement disclosed. No annotations contradict, but description does not explain side effects like duplicate handling or overwrite behavior. Adds type semantics beyond annotations.

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 only, no fluff. First sentence covers action and prerequisite; second sentence elaborates on types. Front-loaded and 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?

Covers main action, auth, and alert types. Output schema exists (per context), so return format omitted. Missing error conditions or subscription limits, but sufficient for use.

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?

Adds real-world meaning to type and threshold params beyond schema descriptions (e.g., 'proactive watcher' for forecast). 100% schema coverage reduces burden, but description enhances understanding.

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?

Explicit verb 'subscribe' with resource 'snow alerts' and signed-in user scope. Distinguishes from siblings like list_alerts and unsubscribe_alerts by stating it creates subscriptions.

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?

States OAuth token requirement and explains each alert type's purpose (e.g., forecast as proactive watcher). Lacks explicit when-not-to-use or alternatives but provides clear 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?

Annotations already indicate destructive action and idempotency. The description adds the auth requirement and the source of the id, adding value beyond annotations.

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 concise sentences, no fluff, front-loaded with the action and source.

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 fully covers the tool's purpose, parameter source, and auth requirement. With an output schema present, no further return info is needed.

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 baseline is 3. The description adds context that the id comes from list_alerts, which aids the agent in knowing how to obtain it.

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 'Remove one of the signed-in user's alert subscriptions by id', with a specific verb and resource. It also ties to list_alerts, distinguishing it from subscribe_alerts.

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 indicates the source of the id (list_alerts) and the required auth token, providing clear context. It does not explicitly state when not to use it, but the use case is straightforward.

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