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

The annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true. The description adds value by naming the data sources (GLEIF and World Bank WGI), the components returned (composite score, tier, MiCA compliance flag, per-source breakdown), and that it pulls from live data. It does not contradict annotations; the description reinforces the read-only, non-destructive nature.

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 compact at three sentences, front-loaded with the core purpose. The second sentence adds sources and output details, and the third sentence names a sibling for differentiation. Every sentence serves a distinct purpose, and there is no extraneous text.

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?

Given the rich annotations (read-only, idempotent, open-world), full schema coverage for all 3 parameters, and an output schema present, the description completes the picture by detailing the specific data sources, output components, and how it relates to sibling tools. The tool is simple and well-documented, so this is fully adequate.

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 has 100% description coverage, with descriptions for 'q', 'lei', and 'country' already present. The description adds minimal extra value beyond the schema: it explains that 'lei' provides fastest response and 'country' improves accuracy. This is a small incremental benefit, so baseline 3 is appropriate.

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 'Get the live governance score (0–100) for any legal entity identified by LEI or company name.' It specifies the exact verb (get), resource (governance score), and scope (legal entities). The description also distinguishes this tool from its sibling 'esg.score' by stating it 'complements esg.score by isolating the G pillar as a standalone institutional-grade signal.'

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 explains when to use this tool: for standalone G pillar governance scores. It also implies an alternative (esg.score) but does not explicitly state when not to use it or provide detailed context about prerequisites. The context signals show no required parameters, so flexibility is implied, but specific guidance on when to use LEI vs q is missing.

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