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

Annotations already establish read-only, idempotent, and non-destructive behavior; the description adds meaningful context beyond those by specifying that the tool returns an annual time-series in MW and that it covers both renewable and non-renewable capacity. It also states the data provenance, which is useful context. There is no contradiction with 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?

The description is concise and front-loaded, with the core action in the first sentence and no restatement of schema fields. The final sentence about IRENA's authoritative status is slightly promotional but still supports source selection, so it does not meaningfully hurt clarity.

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 read-only country-capacity query with an output schema and a thorough input schema, the description covers all essential invocation context: countries, technologies, grid connection, year range, units, return shape, and source. Missing explicit sibling routing and an example are minor gaps given how much is already provided by the schema and annotations.

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 input schema already provides 100% parameter coverage, including ISO country-code format, year ranges, defaults, and detailed technology and grid enums. The description adds general scope context (26 technologies, on/off-grid, 2000–2025) but does not need to explain parameter mechanics further. Baseline 3 is appropriate given the schema's strength.

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 uses a specific verb ('Query'), a concrete resource ('electricity installed capacity (MW)'), and a clear scope ('for any country' from the IRENA IRENASTAT database). It also states the return shape ('time-series of MW values per year'), which leaves little ambiguity. It does not explicitly contrast with sibling tools like capacity_region or generation_country, so it stops just short of full sibling differentiation.

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 clear context: use this tool for country-level installed capacity data, spanning 226 countries, 26 technologies, grid types, and 2000–2025. However, it never says when not to use it or points to alternatives such as irena.energy.capacity_region or irena.energy.generation_country, leaving routing decisions to inference.

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