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

Beyond the read-only/idempotent annotations, the description discloses important behavior: it returns figi_candidates when multiple instruments match, marks unresolved identifiers explicitly, labels identifiers by source, and degrades gracefully when GLEIF or OpenFIGI is unavailable. This gives the agent a clear model of expected results and failure modes.

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 information-dense and front-loaded, but it is a single sprawling paragraph with heavy nesting, parentheticals, and at least one garbled clause ('more than one instrument it asserts nothing...'). The content is mostly valuable, but the structure makes it harder to parse than it should be.

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 there is no output schema, the description compensates well by describing return content: figi_candidates, unresolved, source labels, RxCUI citation, and fallback behavior. It also covers acceptable inputs, supported types, and the internal cascade effect. An agent has enough context to invoke this tool correctly in most realistic scenarios.

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 coverage is 100%, so the baseline is 3, but the description adds substantial value. It explains accepted input formats for company and drug types, gives realistic examples, and warns against passing full noun phrases like 'NEW YORK ST DORM AUTH revenue bonds' instead of the entity name. This is far more actionable than the raw schema.

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 the tool's function: resolving a user-spoken name to canonical identifiers such as CIK, ticker, LEI, FIGI, and RxCUI. It names the supported entity types and gives concrete examples. It does not explicitly differentiate itself from sibling tools like resolve_xref, though the 'identifiers other tools require as input' phrasing implies its role.

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 explicit usage guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains which input forms are accepted and covers edge cases like non-equity instruments. However, it never names an alternative tool or states when not to use this one, so it stops short of full when/when-not guidance.

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