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

Annotations already mark this as read-only, idempotent, open-world, and non-destructive. The description adds substantial behavioral detail: it cascades through multiple lookup endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, returns explicit `unresolved` results, and returns `figi_candidates` when names are ambiguous. No contradiction with 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 long, but dense and purposeful. Purpose and trigger are front-loaded, followed by detailed type-specific behavior and a critical input caveat. Some phrasing, such as the opening quoted examples, is slightly repetitive but still useful for recognizing user intent.

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 complex tool with no output schema, the description covers return content (CIK, ticker, LEI, FIGI, RxCUI, ingredient, brand), source labelling, unresolved and ambiguous cases, accepted input forms, and failure degradation. An agent has enough information to invoke it correctly and interpret its results.

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?

While the input schema already covers both parameters, the description adds high-value semantics: pass only the entity name, use the issuer exactly as printed for bonds, avoid trailing security-class words, accept ticker/CIK/ISIN/name for companies, and accept brand or generic names for drugs. This goes far beyond the schema descriptions.

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 begins with concrete user-phrase examples and clearly states the tool's function: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It also positions it relative to downstream tools ('Use FIRST whenever you have a name but need an ID'), making its purpose and differentiation clear.

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 a clear trigger condition ('Use FIRST whenever you have a name but need an ID') and spells out accepted input forms per entity type. It does not explicitly name an alternative tool to use instead in other cases, but the context is strong enough to guide selection.

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