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

With no annotations provided, the description carries full burden for behavioral disclosure. It reveals that the tool returns daily grain only (no sub-daily), that energy_rev_mlf_adjusted can be null (not zero) when the LP backcast hasn't run, and explains why get_battery_optimal's 'actual' figure is not comparable. This provides solid behavioral context, though it omits details like authorization or rate limits, which are less critical for a read-only data tool.

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 (3 sentences), front-loads the core purpose, then provides usage guidance and a specific note about the MLF-adjusted field. Every sentence adds value without redundancy.

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?

Despite lacking an output schema, the description explains the key output fields (energy_rev, energy_rev_mlf_adjusted) and a critical behavior (null vs zero). It omits a full list of fields but covers the essential information for a revenue query tool. This is nearly complete for the use case.

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 coverage is 100% with each parameter described (e.g., duid example 'HPR1', date format 'YYYY-MM-DD'). The description adds that the date range is for daily grain only, but does not significantly extend parameter meaning beyond what the schema provides. With high schema coverage, baseline is 3.

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 it returns daily gross-spot revenue by market (energy + FCAS) for one battery over a date range. It specifies the resource (battery), metric (revenue), and constraints (daily grain only). It distinguishes from the sibling get_battery_optimal, making the purpose unambiguous.

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 tells when to use this tool ('for total revenue questions') and warns against using get_battery_optimal as a revenue source, explaining why its figures differ. It also notes that MLF-adjusted figures are available here, eliminating the need to switch tools.

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