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

Annotations already establish readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful context beyond that: data source (Energinet), auth expectations ('no auth required'), temporal resolution, and the recent-data default. No statement contradicts the annotations.

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?

Three compact sentences with no filler. The core metric and resolution are front-loaded, followed by filter semantics, a practical use case, and provenance/auth info. Every sentence earns its place.

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-required-parameter read-only lookup with a full output schema and rich annotations, the description covers source, auth, resolution, filter behavior, and intended use. Nothing an agent needs to invoke it correctly 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?

Schema coverage is 100%, so the schema does the heavy lifting. The description still adds value by clarifying the start_date/end_date interplay ('omit both to get the most recent readings') and providing concrete DK1/DK2 price-area examples. This is incremental semantic guidance rather than mere repetition.

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 what the tool does with a specific verb and resource: 'Get 5-minute-resolution CO2 emission intensity... of the Danish electricity grid'. It includes units and price-zone scope. It does not explicitly distinguish itself from the sibling power endpoints (production_consumption, spot_prices), but the CO2/emission-intensity language makes 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 provides a concrete use case ('scheduling energy-intensive tasks during low-carbon periods') and explains the default behavior when both date parameters are omitted. It does not name alternatives or state when not to use the tool, but the context is clear enough for a read-only data retrieval tool.

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