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

Despite having readOnly/openWorld/idempotent annotations, the description adds substantial behavioral detail: graceful degradation when GLEIF/OpenFIGI are unavailable, explicit reporting of unresolved identifiers, returning figi_candidates rather than asserting a match on ambiguity, and the cascade through multiple internal lookup endpoints. This goes far beyond what the annotations alone convey.

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 each section earns its place by covering supported types, input forms, edge cases, ambiguity handling, and degradation behavior. The opening trigger-phrase examples front-load the core purpose, and the structured breakdown avoids being a wall of text. It is somewhat verbose, but not padded.

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 what the tool returns, and it does so thoroughly: CIK, ticker, company_name, LEI with ownership, FIGI, RxCUI with ingredient/brand, source labels, unresolved identifiers, and figi_candidates. It also covers input specificity and fallback behavior for unavailable enrichments, so an agent has enough to call it 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?

Although the schema already covers 100% of parameters, the description meaningfully extends the value parameter semantics with concrete examples ('AAPL', '0000320193', 'ozempic'), clarifies what is accepted for each type, and adds a critical caveat for bond issuers: pass the entity name only, not the full noun phrase, because trailing security-class words fail FIGI matching. This is high-value guidance the schema does not provide.

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 precise verb+resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It goes beyond the title by enumerating concrete query patterns ('ticker for', 'CIK for', 'LEI for') and the two supported entity types. It also clarifies how it differs from lookups by stating it returns canonical identifiers like CIK, LEI, FIGI, and RxCUI.

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 says 'Use FIRST whenever you have a name but need an ID,' and provides many trigger examples that make the intended usage obvious. It does not explicitly name alternative sibling tools like entity_profile or compare_entities, so the when-not-to-use guidance is somewhat implicit, but the selection context is clear.

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