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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals a key behavioral trait: it attempts to use live Open-Meteo UV/cloud data and falls back to a clear-sky model when unreachable. It also lists the main outputs, adding transparency about what the tool returns. It does not cover error handling or rate limits, but the core behaviors are well disclosed.

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 no fluff. It packs the core question, data source fallback, and key outputs into a compact, front-loaded structure. Every clause contributes meaning, and the sentence is well-organized despite its length.

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 8 parameters and no output schema, the description covers the main purpose and gives a high-level list of outputs (UV index, minutes needed, window times). It does not specify the return structure or mention how parameters like age and skin type influence results, but the schema descriptions fill in parameter meaning. The description is sufficient for an agent to select the tool for a real-time status check.

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 baseline is 3. The description itself adds no parameter-specific semantics; it only mentions outputs in general terms. It does not discuss how parameters like age, skin type, or targetIU affect the calculation, but the schema already describes each parameter adequately.

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: assessing whether the current moment is good for vitamin D synthesis at a location. It mentions the data source (live Open-Meteo vs clear-sky model) and key outputs (current UV index, minutes needed, window times). This distinguishes it from siblings like get_sun_forecast and get_vitamin_d_window by emphasizing 'RIGHT NOW' and the live data aspect.

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: use when you need to know if now is a good moment for vitamin D synthesis. The 'RIGHT NOW' framing implies its niche compared to forecast/window tools, and it specifies the live-data and fallback behavior. However, it does not explicitly name alternative tools or when-not-to-use, so it falls 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.