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

Even with readOnly/idempotent annotations already present, the description generously discloses behavior: it cascades through lookup endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, asserts nothing and returns figi_candidates on ambiguous matches, labels each identifier by source, and reports unresolved identifiers in an `unresolved` field. This goes well beyond the annotations and tells an agent what to expect from a call.

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 it is front-loaded with the purpose and 'Use FIRST' guidance before diving into supported types. The design is organized and dense rather than padded; the only minor redundancy is the final sentence saying both that calls cascade through endpoints and replace 2-3 manual lookups.

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 to lean on, the description explains what is returned for each supported type: CIK, ticker, company_name, LEI with ownership, FIGI, and RxCUI with brand/ingredient, plus how unresolved or ambiguous identifiers appear. This is sufficient for an agent to know what to expect and how to use the result in later tool calls.

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 schema already documents `type` and `value`. The description adds meaningful parameter nuance beyond the schema, especially the rule to pass the bond issuer name exactly as printed without trailing security-class words, and the ISIN-to-LEI resolution example, which reduce the chance of malformed lookups.

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 queries ('What's the ticker for…', 'find the CIK for…') and states it 'resolve[s] a user-spoken NAME to the canonical/official identifiers other tools require as input.' This is a specific verb-plus-resource and cleanly separates resolve_entity from siblings like entity_profile, which focus on profiles/context rather than ID resolution.

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?

'Use FIRST whenever you have a name but need an ID' is an explicit, actionable selection rule, and the supported-types section makes the scope clear (company vs. drug). It does not explicitly say when not to use this tool or name a specific alternative, but the context is strong enough for an agent to select it correctly.

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