Query time-series datum

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

Since no annotations are provided, the description carries the burden of behavioral disclosure. It discloses that 'watts' is averaged power (not total) and points to get_energy for true totals, which is a meaningful behavioral nuance. However, it doesn't mention whether the tool is read-only or any potential side effects, though as a query that is implied. It could also disclose behavior around multiple sources or response size, but the key nuance is covered.

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, front-loaded with the core purpose, and every sentence adds value. It packs essential information into three sentences with no fluff, and the alternative tool reference is clearly positioned at the end. Structure is effective for quick agent parsing.

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 query tool with 5 parameters all documented, no output schema (so the description's summary of returned metrics is essential), and guidance on parameter selection, the description is complete. It covers what the tool does, how to use it, and where to go for alternative functionality. There are no missing pieces an agent would need to call it correctly.

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 covers all parameters with descriptions (100% coverage), so baseline is 3. The description adds value by providing aggregation selection guidance (Day, Hour, Month) that goes beyond the enum values, effectively helping the agent choose the right parameter value. It also clarifies the semantics of the 'watts' output, which is related to the data returned. This elevates it above the 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 clearly states the tool queries time-series data over a date range with aggregation, accepts multiple nodes, and returns specific metrics (mean power, total energy, mean irradiance). It uses specific verbs and resources, and distinguishes itself from get_energy by clarifying the difference between averaged power and true energy totals.

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 explicit guidance on when to use different aggregation periods (Day for week-to-month, Hour/FifteenMinute for single-day shape, Month for year trends) and explicitly names an alternative tool (get_energy) for energy totals, telling the agent when NOT to use this tool.

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