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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses meaningful behaviors: graceful degradation when GLEIF or OpenFIGI are unavailable, explicit `unresolved` fields, return of `figi_candidates` on ambiguous matches, and internal cascading through multiple lookup endpoints. These traits are valuable to an agent invoking the tool and are not encoded in the annotations or schema.

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 front-loaded with purpose and query examples, and nearly every clause carries operational specificity. It is long and dense with parentheticals, which adds reading difficulty, but the complexity is largely warranted given two entity types, multiple identifier sources, and important edge-case behaviors.

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 thoroughly covers return contents for both entity types, including CIK, ticker, company name, LEI/ownership, FIGI, and RxCUI/brand/ingredient. It also explains ambiguous matches, unresolvable identifiers, and degraded enrichment, so an agent has enough context to call the tool correctly even in failure or edge cases.

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 schema gives only a generic value string, but the description substantially expands parameter meaning: it lists valid forms (ticker, CIK, ISIN, brand/generic name), explains the ISIN-to-LEI mapping, and instructs agents to pass only the entity name, with a concrete bond example explaining why trailing security-class words fail. This is far beyond the schema's 100% descriptive coverage.

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 defines a specific action—resolving user-spoken names to canonical identifiers—and enumerates the exact identifier sets produced for company and drug types. The query examples and accepted-input list make the purpose unambiguous and distinct from sibling tools that focus on profiles, comparisons, or searches.

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 trigger: 'Use FIRST whenever you have a name but need an ID.' It also clarifies accepted inputs and cautions against passing full noun phrases, which is strong practical guidance. It does not name alternative sibling tools with when-not-to-use conditions, but the when-to-use direction is clear and actionable.

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