Sunshine Atlas

Find sunny destinations

find_sunny_destinations

Ranked answer to "where is it sunny (and warm) in ?" — destinations sorted by that month's 0–100 Sunshine Score (long-term climate normals). Filter by continent or country, minimum daytime temperature, population, swimmable sea (≥21°C), or a ceiling on the midday UV index. Every result carries that month's temperatures, rain, midday UV (WHO scale) and sea temperature, plus a citable thesunshinemap.com URL.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	How many results (default 10, max 50)
`month`	Yes	Month name ("November", "nov") or number 1–12
`where`	No	Optional continent ("Europe", "Asia", "North America", …) or country (name or ISO-2 code) to search within. "Europe" uses the traveler definition and includes the Canary Islands.
`max_midday_uv`	No	Only places whose typical midday UV index that month is at or below this (WHO scale: ≤2 low, ≤5 moderate, ≤7 high). For sun-sensitive travellers — "sunny but not fierce".
`min_day_high_c`	No	Only places at least this warm by day that month, °C
`min_population`	No	Only places with at least this population (default 0 = include small islands and outposts)
`require_swimmable_sea`	No	Only coastal places with sea ≥21°C that month

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the burden. It discloses the ranking metric (Sunshine Score from long-term climate normals), the availability of filters, and the data every result carries (temperatures, rain, UV, sea temp, URL). It also explains special semantics like the traveler definition of Europe and the swimmable sea threshold. It does not cover edge cases like pagination or errors, but for a search tool this is sufficient.

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

Conciseness5/5

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

Two tightly packed sentences. The first delivers the core purpose and ranking logic, the second summarizes filters and outputs. No fluff, no redundancy, fully front-loaded.

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?

For a 7-parameter tool with no output schema and no annotations, the description covers the essential context: what the tool returns (temperature, rain, UV, sea temp, URL), how results are ordered (Sunshine Score), and what filters exist. This is enough for an agent to select and invoke the tool correctly, even without an output schema.

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?

Schema description coverage is 100%, so baseline is 3. The description adds a summary of filter categories (continent/country, min temp, population, sea, UV) and the 0–100 Sunshine Score context, but does not add details beyond what the schema already provides for individual parameters. Thus, it meets the baseline without exceeding it.

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 opens with a specific verb+resource+scope: 'Ranked answer to where is it sunny and warm in <month>?' and explains the Sunshine Score sorting. This clearly distinguishes it from siblings (compare_destinations, get_destination_climate) by focusing on discovering destinations rather than comparing or retrieving single-location climate.

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

Usage Guidelines4/5

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

It provides clear context: use when asking for sunny/warm places in a given month, with optional filters. It does not explicitly name alternatives or say when not to use, but the purpose is so specific that usage is implied. Lacks explicit exclusions, so not a 5.

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

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TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: compare_destinations for side-by-side comparison, find_sunny_destinations for ranked search, and get_destination_climate for a single destination's full climate data. No overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: compare_destinations, find_sunny_destinations, get_destination_climate. The naming is uniform and predictable.

Tool Count5/5

With 3 tools covering search, comparison, and detailed lookup, the count is well-scoped for the server's purpose. Each tool adds unique value and there are no redundant or missing operations.

Completeness5/5

The toolset fully covers the domain of sunshine/climate comparison: finding sunny places, comparing specific destinations, and retrieving complete monthly climate details. No obvious gaps or dead ends.

Resources

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