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

Annotations already declare read-only, idempotent, and non-destructive behavior, and the description adds rich behavioral detail: ambiguous matches return figi_candidates rather than assertions, unresolved identifiers remain visible under unresolved, enrichment degrades gracefully, and calls cascade through multiple internal endpoints. No statement contradicts the annotations.

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 definition is front-loaded: purpose and usage rule appear immediately, followed by structured type-by-type detail. It is long and dense, forming a wall of text in places, but nearly every clause carries useful behavioral or semantic information rather than padding.

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?

For a multi-source resolver with no output schema, the description explains what the agent should expect: CIK, ticker, company_name, LEI, FIGI, RxCUI, figi_candidates, unresolved, source labels, and a citation URI. It also covers failure modes and graceful degradation, so the agent can set expectations even without an output schema.

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 has 100% parameter coverage, the description adds substantial meaning: it reveals that company inputs may also be ISINs, which the schema omits, and it warns to pass the issuer exactly as printed rather than the full noun phrase. It also gives concrete examples for both company and drug values, materially reducing misuse.

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 natural-language queries and clearly states the deliverable: resolving a user-spoken name to canonical/official identifiers needed by other tools. It also distinguishes itself from siblings by positioning itself as the first-step ID resolver (Use FIRST...), and enumerates exact outputs such as 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?

It gives an explicit trigger: Use FIRST whenever you have a name but need an ID, and it documents accepted inputs for both company and drug lookups. However, it never names sibling tools like entity_profile or compare_entities or states when to prefer them, so it provides strong context without explicit exclusions.

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