Find a city in the app's database by name (any of the app's six languages works) and get its coordinates, IANA timezone and elevation — feed those into the other tools.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | City name, e.g. 'Madrid', 'London', 'Nueva York' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It explicitly discloses the tool's behavior: accepts city names in any of six languages and returns coordinates, timezone, and elevation. It does not describe error cases or read-only guarantees, but for a search tool this is adequate. No contradictions with annotations (none present).
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, focused sentence. It front-loads the verb and resource, then provides essential output details and usage context. Every clause is informative and no redundant wording is present.
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 (one parameter, no output schema), the description is complete for an agent to understand its role and output. It mentions the return values and integration with other tools. It could optionally detail return format or failure behavior, but those are handled by schema and reasonable inference. Overall, sufficient.
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 already has 100% description coverage with a clear example and format. The tool description adds the multilingual aspect ('any of the app's six languages works') and the purpose of the query, which enhances understanding but doesn't add significant syntax details 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 verb 'find' and the resource 'city in the app's database', and specifies the output (coordinates, IANA timezone, elevation). It distinguishes itself from sibling tools focused on sun/vitamin D by being the city lookup utility. The phrase 'feed those into the other tools' confirms its role as a prerequisite lookup.
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 when to use this tool: to obtain city data needed by other tools. It doesn't explicitly state exclusions or alternatives, but the context is clear given the sibling list. The instruction 'feed those into the other tools' provides practical usage direction.
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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Every tool has a clearly distinct purpose: year comparison vs. single-year vs. single-day vs. multi-day forecast vs. pure sun times vs. current status; personal history/profile tools are cleanly separated from public location tools. No two tools appear to do the same thing.
All tool names follow a consistent verb_noun snake_case pattern (get_, set_, log_, search_, update_, configure_, estimate_, compare_). This makes the toolset highly predictable and easy to navigate.
At 15 tools, the count sits at the upper edge of the ideal range, but each tool carves out a distinct niche within the vitamin D/sun exposure domain. The scope is broad, yet no tool feels extraneous; a slightly lower score reflects the borderline-high number.
The domain is well covered: location search, sun times, vitamin D windows for day/year/forecast, session estimation, personal profile read/update, history logging and correction. Minor gaps exist (e.g., no favourite city management, no way to delete history), but core workflows are fully supported.