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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It explains how rev_per_mw_yr is annualised, why the figure is distorted for commissioning units and small FCAS-primary or shorter-duration batteries, and warns against trying to 'correct' the metric by rescaling. It even discloses that results are null until the first background refresh completes.

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 long, but almost every sentence carries load-bearing caveat that prevents misuse of a genuinely misleading metric. The main purpose is front-loaded, and the warnings are ordered from global caveats to specific namespace use cases. A little more structure would help, but the length is justified.

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 zero-parameter tool with no output schema, the description is unusually complete. It defines the exact metric, its data source, its denominator, its distorting conditions, the wrong use cases, the corrective tools, and the special requirement to inspect commercial_context before comparing units. Nothing an agent needs for correct selection or safe interpretation is missing.

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?

With zero parameters and an empty input schema, there is nothing for the description to add beyond what the schema already communicates. The description does, however, usefully explain the meaning and caveats of the flagship return field 'rev_per_mw_yr', which is the only place where parameter semantics could matter.

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 action and resource: 'List every NEM-registered grid-scale battery' and enumerates the returned fields, including DUID, station, region, MW/MWh, MLF, coordinates, and commercial context. This clearly distinguishes it from the sibling get_battery_* tools, which target individual batteries or revenue analysis.

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 explicitly says when not to use the tool: 'Do not use it to compare units, rank performance, or answer which battery earns most.' It names the alternatives directly — get_battery_revenue and get_battery_optimal — and requires checking commercial_context before comparing units, which is concrete actionable guidance.

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