Where is it sunny? Monthly sunshine scores, temps, rain, UV and sea temp for 3,833 destinations.
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Average 4.3/5 across 3 of 3 tools scored.
Each tool has a clear, distinct purpose: comparing destinations, finding sunny spots, or retrieving detailed climate data. No overlap.
All tool names follow a consistent verb_noun pattern with underscores: compare_destinations, find_sunny_destinations, get_destination_climate.
With 3 tools, the set is small but well-scoped for a focused domain. Each tool serves a core query type without redundancy.
The tools cover the main use cases: comparing multiple destinations, querying by criteria, and getting full climate data. No obvious gaps for the intended purpose.
compare_destinationsCompare destinationsAInspectSide-by-side sunshine/climate comparison of 2–5 destinations — overall or for one month ("Algarve or Crete in October?"). Returns each place's Sunshine Score, temperatures, rain, midday UV index and sea temperature, plus a one-line verdict of which is sunnier.
| Name | Required | Description | Default |
|---|---|---|---|
| month | No | Optional month to compare in; omit for year-round comparison | |
| destinations | Yes | 2–5 destinations (city, "city, country", or IATA code) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the output fields (Sunshine Score, temperatures, rain, UV index, sea temperature, and a verdict), which is helpful. However, it does not mention any limitations, potential errors (e.g., invalid destinations), or explicitly state that it is a read-only operation. The description is not misleading, but it lacks deeper behavioral context beyond the output summary.
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 and efficiently structured: two sentences. It front-loads the core purpose ('Side-by-side sunshine/climate comparison'), then elaborates on scope and output. Every sentence earns its place with no fluff or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, but the description explicitly enumerates the return values (Sunshine Score, temperatures, rain, UV, sea temperature, verdict), which mitigates that gap. It also covers the optional month parameter and the range of destinations. It is not fully complete—it doesn't describe error behavior or data sources—but for a comparison tool with this simplicity, the key aspects are covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the parameters are already fully documented: destinations (array, min 2, max 5) and month (optional, omit for year-round). The description adds an example phrase but does not introduce new semantic meaning beyond what the schema provides. Thus, 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 performs a side-by-side comparison of 2–5 destinations, focusing on sunshine/climate metrics. It distinguishes itself from sibling tools by emphasizing comparison (find_sunny_destinations searches for sunny places, get_destination_climate likely focuses on a single destination). The verb 'compare' and resource 'destinations' are specific and unambiguous.
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 clear context on when to use the tool: for comparing multiple destinations, either year-round or for a specific month, with an illustrative example ('Algarve or Crete in October?'). It does not explicitly name alternative tools or exclude them, but the comparative scope (2–5 destinations) inherently distinguishes it from siblings. It could benefit from an explicit 'use this when you need to compare multiple places' recommendation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_sunny_destinationsFind sunny destinationsAInspectRanked 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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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 |
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.
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.
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.
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.
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.
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.
get_destination_climateDestination climate & best time to visitAInspectFull climate card for one destination: 0–100 Sunshine Score, day/night °C, rainfall, midday UV index (WHO scale) and sea temperature for all 12 months, plus its sunniest month ("best time to visit" for sunshine), annual sunshine hours and peak midday UV. Accepts a city name ("Faro"), "city, country" ("Nice, France") or IATA airport code ("FAO").
| Name | Required | Description | Default |
|---|---|---|---|
| destination | Yes | City name, "city, country", or 3-letter IATA airport code |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly lists the output data fields and accepted input formats, but it does not address error behavior, data source, or any read-only implications. It is adequate but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact: two sentences with no filler. The first sentence front-loads the tool's core purpose and outputs, while the second covers input formats. 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 there is no output schema, the description compensates by enumerating all returned metrics and the sunniest month, which is crucial for an agent. It also specifies input format variants. It is complete for the tool's apparent simplicity, though it leaves out edge-case handling, which is a minor 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 schema already describes the parameter with 100% coverage, but the description adds concrete examples ('Faro', 'Nice, France', 'FAO') and reiterates the three accepted formats, which helps clarify exactly how to format the destination value. This goes beyond the schema's baseline.
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 unambiguously states the tool provides a 'Full climate card for one destination' and enumerates the specific metrics (Sunshine Score, temperatures, rainfall, UV, sea temperature). It clearly distinguishes from siblings by emphasizing 'one destination' as opposed to comparison or discovery 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 clearly implies usage for a single destination's climate details, effectively distinguishing it from sibling tools like compare_destinations and find_sunny_destinations. However, it does not explicitly mention when to prefer this tool over alternatives or provide explicit exclusions, so it stops short of 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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