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

With no annotations, the description carries the full burden and does well: it discloses granularity options, the constituent generation sources, the half-hourly cadence, the 48h forecast ceiling, and that a lowest-carbon slot is identified. It omits upstream data provenance, rate limits, and error behavior for invalid postcodes, which are the remaining gaps for a zero-annotation 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?

Two sentences with no filler: the first front-loads what the data is and what it contains, the second front-loads the use case. Every clause adds information an agent would otherwise have to guess.

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 two-parameter read tool with an output schema already documenting return shape, the description covers scope, granularity, cadence, and forecast horizon adequately. Minor remaining ambiguity — default hours value and postcode validation behavior — keeps it from a 5.

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 0%, so the description must compensate, and it does: 'up to 48h' bounds the hours parameter, and 'nationally or for a postcode region' explains that a null postcode yields national data while a value yields regional data. It does not state the 24h default or the postcode format/syntax, so it is strong but not complete.

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?

Names a specific resource and metric (Great Britain electricity carbon intensity in gCO2/kWh), states scope (national or postcode region), and enumerates what comes back (generation mix, half-hourly 48h forecast, lowest-carbon slot). This clearly separates it from nearby siblings such as eu_power_mix and eu_power_prices without needing to inspect the schema.

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?

Supplies a concrete decision context — 'Use to schedule EV charging, heat pumps or compute jobs' — which tells an agent when this data is the right input. It stops short of naming an alternative tool or stating when not to use it, so it falls just below the explicit when/when-not bar.

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