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

Annotations already mark the tool as read-only, idempotent, and non-destructive; the description adds substantial behavioral detail beyond those hints. It discloses internal cascading lookups, graceful degradation of LEI/FIGI enrichment, explicit `unresolved` reporting, ambiguity handling via `figi_candidates`, and source labeling of identifiers. No contradiction with annotations exists.

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 well-structured: it front-loads the core use case, then organizes supported types and edge behaviors under clear labels. Most sentences carry operational value, though some parenthetical explanations and examples could be trimmed without losing meaning. It earns its length due to the tool's complexity, but it is not maximally concise.

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 carries the burden of explaining return semantics, and it does so thoroughly. It specifies what identifiers are returned for each type, how unresolved identifiers are surfaced, how ambiguity is handled, and how failures degrade. The tool is complex, and the description covers the operational knowledge an agent needs to call it correctly and interpret 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?

Although the schema already describes both parameters, the description adds significant semantic value beyond it. It details accepted input forms for company type (ticker, CIK, ISIN, name), clarifies that only the entity name should be passed rather than the full noun phrase, and provides concrete examples like 'ozempic' and 'NEW YORK ST DORM AUTH'. This substantially improves an agent's ability to populate `value` correctly.

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 opens with concrete user phrasings and a specific action: resolving a user-spoken name to canonical identifiers. It clearly labels supported entity types and distinguishes itself from sibling tools by saying the identifiers it produces are what 'other tools require as input.' This is a specific verb + resource with strong sibling differentiation.

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 explicitly instructs 'Use FIRST whenever you have a name but need an ID,' giving a clear trigger condition. It also explains when the tool is preferable to manual lookups and covers edge cases like bond issuer names, but it does not name alternate sibling tools or state when to choose them instead. Clear usage context, but no explicit when-not-to-use guidance against alternatives.

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