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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive, but the description adds substantial behavioral context beyond those hints: it cascades through multiple lookup endpoints, degrades gracefully if GLEIF/OpenFIGI are unavailable, returns `unresolved` rather than omitting failures, and asserts nothing when a name matches multiple instruments. This is exactly the added value expected beyond 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 it is front-loaded with the core purpose and usage rule before the deep supported-type details. Some parenthetical density and repeated examples add length, yet each sentence conveys a distinct operational constraint or capability, so the length is largely justified.

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?

There is no output schema, so the description carries the burden of explaining return behavior, and it does so thoroughly: returned identifiers are labeled by source, unresolved identifiers are listed explicitly, `figi_candidates` is returned for ambiguous matches, and even citation format for drug lookups is mentioned. An agent has enough information to call this tool and interpret its result correctly.

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?

Even though the input schema already describes both parameters, the description adds meaning the schema cannot: examples of accepted values (AAPL, 0000320193, CH0038863350, ozempic), the ISIN-to-LEI behavior, and the critical instruction to pass only the entity name and never the full noun phrase. These details materially improve invocation correctness.

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 ('resolve') and resource ('entity name to canonical/official identifiers'), then enumerates exactly what it produces (CIK, ticker, LEI, FIGI, RxCUI) for each supported type. It also differentiates itself from sibling tools by positioning itself as the provider of IDs that 'other tools require as input.'

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 an explicit usage rule: 'Use FIRST whenever you have a name but need an ID.' It also explains what not to do by example, such as passing an issuer name for a bond and warns against including trailing security-class words. This tells the agent both when to invoke the tool and how to prepare valid input.

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