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

The description goes well beyond the annotations by disclosing key behaviors: it returns figi_candidates and asserts nothing on ambiguous matches, labels identifiers by source, states unresolved identifiers explicitly, and degrades gracefully if LEI/FIGI enrichment is unavailable. This is exactly the kind of behavioral context an agent needs and the annotations do not provide.

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 dense and front-loaded with query examples and the core purpose. Although it is long, nearly every sentence carries useful information, including edge-case behavior and supported sources. It slightly over-elaborates inside long parentheticals, but remains organized and readable.

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 tool with no output schema, the description thoroughly explains what the agent should expect: source-labelled identifiers, an unresolved array, ambiguous figi_candidates, and graceful degradation. It also enumerates accepted input forms for both entity types and gives explicit value-format guidance. Nothing critical is missing for correct invocation.

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?

The schema already covers both parameters at 100%, but the description adds meaning beyond the schema: it documents that 'company' also accepts ISINs and explains that non-US issuers resolve via GLEIF, and it gives brand/generic examples for 'drug'. It also explains why the value should be only the entity name, which helps prevent incorrect bond 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 uses a specific verb-plus-resource framing: it resolves a user-spoken NAME into canonical/official identifiers that other tools need, and it explicitly distinguishes itself from siblings by naming supported entity types. It also includes concrete query examples ('ticker for…', 'CIK for…'), which makes the purpose immediately recognizable.

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 usage rule — 'Use FIRST whenever you have a name but need an ID' — and clarifies that it replaces 2–3 manual lookups. However, it does not name sibling tools as alternatives or state when NOT to use this tool, so there is room for more explicit exclusion guidance.

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