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Hypothetical battery revenue

get_simulator_revenue
Read-onlyIdempotent

Modelled revenue for a hypothetical 1 MW battery of a given duration in one region (not a real unit), from NEMPulse's optimal-dispatch model on real prices. Returns P10/P50/P90 revenue for three tiers: realistic (forecast-informed result scaled by the capture ratio real batteries achieve, the only tier to use as a project case), forecast_informed (idealised execution on AEMO's public price forecast) and perfect (an unreachable ceiling). Also energy_share and fcas_share, a monthly series, and the real-unit cohorts behind the FCAS cap and the capture ratio. Gross spot revenue only, before costs, degradation and financing. It is a screening estimate, not a forecast of future revenue.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regionYesNEM region code: NSW1, QLD1, SA1 (South Australia), TAS1 or VIC1.
durationYesBattery duration in hours: 1, 2 or 4.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare a safe read-only idempotent profile, and the description adds substantial context beyond them: the gross-revenue boundary (before costs, degradation, financing), the tier definitions and their reliability, the limitations of the FCAS cap and capture-ratio cohorts, and the screening (not forecasting) nature. This is exactly the extra behavioral disclosure an agent needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Dense but front-loaded: what it is, whose data, then the return shape, then caveats. Every sentence carries information, though the tier explanation is somewhat compressed and the block could be broken into shorter units for scanning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description carries the full burden of explaining returns, and it does: tiers, P10/P50/P90, energy_share/fcas_share, monthly series, and supporting cohort data. An agent know exactly what comes back and how much to trust it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with enums on both parameters, so region codes and duration values are already fully documented in the schema. The description adds only a light restatement ('of a given duration in one region') without extra syntax or constraint detail, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource (modelled revenue for a hypothetical 1 MW battery) and immediately scopes it as 'not a real unit', which distinguishes it from the sibling get_battery_revenue. The modelled/optimal-dispatch framing is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives strong guidance on the returned tiers ('the only tier to use as a project case', 'an unreachable ceiling') and when not to rely on the result ('a screening estimate, not a forecast'). It stops short of explicitly naming the sibling to use for real units, but the 'not a real unit' framing routes the agent away from get_battery_revenue clearly enough.

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

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