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

Annotations already declare readOnly/openWorld/idempotent, and the description adds substantial behavioral detail beyond them: the internal cascade across lookup endpoints, graceful degradation if GLEIF/OpenFIGI are unavailable, explicit listing of unresolved identifiers, and ambiguity handling that asserts nothing and returns candidates. It also discloses that non-equity instruments resolve here, which is a non-obvious behavior.

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 and front-loaded with purpose and usage priority. It uses chunked sections (SUPPORTED TYPES, enrichments, degradation, internal behavior) that make the density navigable. Some parenthetical asides are long, but each contributes substantive guidance rather than fluff.

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?

Given the tool's complexity — multiple entity types, multiple identifier sources, ambiguity and failure cases — and the absence of an output schema, the description is remarkably complete. It explains what identifiers come back for each type, what happens on unresolved or ambiguous names, and how partial failures behave. An agent has enough 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?

Schema description coverage is 100%, but the description goes well beyond the schema: it gives concrete input examples (AAPL, 0000320193, ozempic, metformin), clarifies that an ISIN resolves to the issuing legal entity, and provides critical value-format constraints such as passing the entity name only, not the noun phrase. This meaningfully compensates for and enriches the structured schema.

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 states a specific verb+resource: resolve a user-spoken name to canonical/official identifiers that other tools require as input. It opens with concrete example queries and explicitly frames this as the tool to use when you have a name but need an ID, distinguishing it from entity lookup/profile tools like entity_profile and compare_entities.

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 explicit guidance: "Use FIRST whenever you have a name but need an ID." It also provides type-specific usage rules, such as passing the issuer name exactly as printed for bonds and never the full noun phrase, and explains what happens on ambiguity (returns figi_candidates rather than asserting). This is clear when-to-use and how-to-use guidance, even without naming sibling exclusions.

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