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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, fully covering the safety profile. The description adds value beyond that by specifying coverage ('233 countries/regions from 2000 to 2025'), the data source (IRENASTAT), and the real-world meaning of the two indicators. No contradictions with annotations; an output schema covers the return format.

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?

Four sentences with the core purpose front-loaded in sentence one. Coverage, indicator semantics, and use-case context each occupy exactly one sentence with zero redundancy. The final use-case sentence earns its place by informing tool-selection decisions rather than padding.

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?

Rich annotations (read-only, idempotent, open-world) plus a full output schema mean the description only needs to carry selection and scoping information, which it does well. The one gap is explicit routing between this tool and its IRENA siblings — an agent comparing names could wonder which to use for absolute values, though the 'share (%)' wording largely resolves this.

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 description coverage is 100% with detailed parameter docs (ISO codes, region examples, defaults, year bounds), so the baseline is 3. The description elevates this by explaining the conceptual difference between the indicator enum values — 'generation' meaning electricity actually produced versus 'capacity' meaning installed plant capacity — which is exactly the kind of semantic context an agent needs to choose correctly.

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 opens with a specific verb ('Query'), a precise resource ('share (%) of renewable energy in total electricity capacity or generation'), and a defined scope ('any country or IRENA world region'). The 'share (%)' phrasing implicitly distinguishes this from sibling tools like irena.energy.capacity_country and irena.energy.generation_country, which by name return absolute values, making the tool's unique role evident.

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 gives concrete context for when the tool is relevant ('Key metric for energy transition tracking, SDG 7 monitoring, comparison against national climate targets') and operational guidance for choosing the indicator enum. However, it never names the sibling IRENA tools or states when to prefer them (e.g., when absolute MW/GWh values are needed), so the when-not-to-use dimension is absent.

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