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

Beyond the readOnly/idempotent annotations, the description surfaces important behaviors: enrichment degrades gracefully when GLEIF/OpenFIGI are unavailable, ambiguous matches return figi_candidates instead of asserting, unresolved identifiers are explicitly listed under `unresolved`, and each call cascades through multiple lookup endpoints. This is exactly the kind of behavioral context annotations cannot 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 and dense, with many parentheticals, but it is front-loaded with user-intent examples and every sentence conveys a distinct fact needed for correct usage. Some redundancy exists, such as repeating the 'never the question's full noun phrase' point in the parameter description, but overall the length is justified by the tool's multi-source complexity.

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 full burden of explaining what the agent should expect back. It covers resolution targets, source provenance labeling, ambiguous-name behavior, unresolved-identifier behavior, supported entity types, input formats, fallback degradation, and the fact that each call replaces multiple manual lookups. This is sufficient for an agent to select and invoke the tool 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 both parameters at 100%, the description adds substantial meaning: accepted input formats for company (ticker, CIK, ISIN, name), drug brand/generic examples, and a critical negative example for bonds telling the agent to pass the issuer exactly as printed and never the full noun phrase. This materially improves invocation accuracy.

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 query examples and then states a precise job: resolve a user-spoken NAME to canonical/official identifiers other tools require as input. This clearly differentiates it from sibling tools like entity_profile and compare_entities, which are about profiling or comparing entities rather than converting names to IDs.

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,' which is a strong, actionable usage rule. It does not, however, name the alternative tools or state when NOT to use resolve_entity (e.g., when you need a profile rather than an identifier), so it stops short of full exclusion guidance.

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