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, so the bar is lower. The description goes well beyond that by disclosing cascading internal lookups, graceful degradation when LEI/FIGI enrichment is unavailable, ambiguous-match behavior with `figi_candidates`, explicit `unresolved` fields, and source labelling. This gives agents a realistic model of tool behavior beyond the annotation hints.

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 and dense, but it is front-loaded with purpose and trigger examples, then structured by entity type. It contains a few sprawling parentheticals and explanatory asides that could be tightened, but nearly every sentence adds necessary detail for correct invocation. It is more verbose than ideal yet earns most of its length.

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?

With no output schema, the description must carry the burden of explaining return behavior. It covers what identifiers are returned, how ambiguity is handled, the meaning of `unresolved`, source attribution, and degradation fallbacks. Combined with the rich parameter guidance, an agent has enough information to invoke the tool 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?

Schema coverage is 100% and the schema gives useful descriptions, but the tool description adds substantial semantics: concrete examples for ticker/CIK/name, the warning about trailing security-class words for bonds, the distinction between brand and generic drug names, and the directive to pass only the entity name. This significantly enriches parameter understanding beyond the 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 uses a specific verb-resource pair: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It enumerates example queries, supported types, and explicitly contrasts with sibling tools by stating 'Use FIRST whenever you have a name but need an ID.' This makes the tool's unique role unmistakable.

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?

It provides strong when-to-use guidance: 'Use FIRST whenever you have a name but need an ID,' plus concrete examples of triggering phrases and an explicit note about the type of input to pass ('Pass the ENTITY NAME ONLY'). However, it does not name specific sibling alternatives or state when NOT to use this tool in favor of others, so the exclusion guidance is incomplete.

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