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

With no annotations, the description discloses the data source (Energinet), the resolution of each returned value, and the notable payment requirement (0.005 USD per call via x402) — all information an agent cannot get from structured fields. It does not cover error behavior for an invalid area or any rate limits, so it falls short of full disclosure.

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?

One dense, front-loaded sentence: resource and units first, then scope, then the three return components, then source and cost. No filler sentences.

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?

There is no output schema, and the description compensates by enumerating what comes back (5-minute value, hourly average, forecast with greenest hour) plus units, source, and cost. That is enough for an agent to call it correctly and interpret the result.

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?

The single parameter's schema description is just 'area' with no enum or type constraint, so the description carries real weight by naming DK1/DK2 as the valid areas. It does not spell out whether 'area' is a string value or how the zone is passed, which is a minor gap.

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?

States a specific resource (CO2 intensity of Danish electricity in g CO2/kWh), the exact scope (DK1/DK2), and the three returned measures (latest 5-minute value, last-hour average, forecast with greenest hour). This clearly separates it from price siblings like dk_power_price and dk_power_cheapest_window.

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?

Usage is implied: an agent would reach for this for carbon-intensity questions rather than price questions. However, the description never explicitly says when to choose it over dk_power_price/dk_power_cheapest_window, nor any prerequisites or exclusions.

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