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

With no annotations, the description carries the full behavioral burden, and it delivers: it discloses the surprising month-to-date default behavior, names exactly which fields are affected, explains the date-scoping mechanism, and warns about the incomparability of merchant vs tolled/contracted revenue figures. This far exceeds what the schema or annotations provide.

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 dense but every sentence earns its place: it opens with the core purpose, then warns about the most dangerous semantic trap, then gives the fix, then routes the user to the alternative tool, then cautions about comparison integrity. No filler or repetition; the structure is front-loaded with the most decision-critical information.

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 no output schema and no annotations, the description is remarkably complete. It documents the key return categories, the specific fields affected by date scoping, the parameter contract, the sibling alternative, and the necessary commercial_context check. An agent can reasonably call the tool correctly without additional context.

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?

Even though schema coverage is 100%, the description adds genuine meaning beyond the schema: it explains that date_from and date_to must both be provided together, that omitting them invokes the month-to-date default, and which returned metrics are scoped by them. The duid example and the commercial_context field also add semantic context not present in the schema alone.

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 states a specific, actionable purpose: 'Deep-dive metrics for one battery by DUID', with examples (HPR1 = Hornsdale) and a clear return scope (revenue, dispatch, SOC, FCAS). It distinguishes itself from sibling tools by explicitly routing daily time-series needs to get_battery_revenue, so an agent can tell it apart from its siblings without opening schemas.

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 gives explicit usage guidance: pass date_from and date_to together to scope the window, omit them for month-to-date defaults, and 'For a true daily time series use get_battery_revenue.' It also warns not to report month-to-date values as 'today's revenue' and to check commercial_context before cross-unit comparisons. This is explicit and actionable.

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