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SnowSure — Snow & Ski

by ai.snowsure

Typical snow by month

get_monthly_snow
Read-onlyIdempotent

Typical snow for a resort MONTH BY MONTH, from ~30 years of ERA5 reanalysis — average snowfall, snow days, base and peak depth, and biggest storm, plus per-season totals. Use for date-choosing questions: 'what is February usually like at Vail', 'when should I go', 'is January or March better'. This is HISTORY, not a forecast and not a prediction — for the next 14 days use get_weather_forecast, and for right now use get_resort. Months with too little history are withheld rather than shown thin; every month returned carries yearsTracked so you can cite the evidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesResort slug

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
markdownNoHuman-readable markdown summary of the tool result (may be omitted when structuredContent carries a typed payload; content[0].text always has the prose).

TDQS

A4.6/5.0
Behavior5/5

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.

Conciseness4/5

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.

Completeness5/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines5/5

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.

All SnowSure — Snow & Ski toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Multiple tool clusters have unclear boundaries: get_resort overlaps with get_resort_info and get_resort_photos, ask_snowdata competes with compare_resorts and the El Niño tools, and get_snow_history / get_season_leaderboard / get_insights cover similar retrospective ground. The descriptions try hard to route agents, but with 45 tools an agent will frequently have to choose between near-equivalent options.

Naming Consistency4/5

Most tools follow a clear verb_noun snake_case pattern: get_resort_info, compare_resorts, find_best_powder, book_lodging, save_resort. Minor deviations like ask_snowdata and the very similar get_resort vs get_resort_info names prevent a 5, but overall the naming convention is predictable.

Tool Count2/5

45 tools is well above the 25+ threshold and puts a heavy selection burden on the agent. While the domain is broad, many tools are narrow variations on conditions, history, road data, or trip planning that could be consolidated or exposed as configurable parameters.

Completeness4/5

The toolkit covers an impressively wide lifecycle: resort discovery, photos, live conditions, forecasts, history, comparisons, passes, flights, lodging, road status, alerts, and user saved resorts. Minor gaps exist such as no lift-ticket booking, no lesson/rental booking, and flight search only produces links rather than a booking flow, but agents can generally complete core snow-trip workflows.

Resources