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

Beyond the annotations (readOnlyHint=true, idempotentHint=true, openWorldHint=true), the description discloses rich behavioral details: cascading internal lookup endpoints, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` reporting, source-labelled identifiers, and the `figi_candidates` behavior when a match is ambiguous. This gives the agent a strong mental model of what will happen at runtime.

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, packed with nested parentheticals, which makes scanning harder than ideal. However, nearly every clause carries necessary procedural information, and the structure is well-organized around SUPPORTED TYPES. The length is largely justified by the tool's complexity, so it earns a 4 rather than a lower score.

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 only 2 parameters, no output schema, and no nested objects, the description covers all essential runtime behavior: input formats, edge cases, ambiguity handling, unresolved identifiers, and partial failure modes. Combined with the read-only/idempotent annotations, an agent has everything needed to invoke and interpret the result 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?

Even though schema coverage is 100%, the description adds significant meaning beyond the bare param names. For `value`, it provides concrete examples for both company and drug types, warns that trailing security-class words will match nothing, and explains the ISIN-to-LEI mapping. For `type`, it connects each enum value to its full resolution behavior, making the parameters far more usable.

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 ('resolve') and resource ('user-spoken NAME to canonical/official identifiers'), and opens with realistic query examples. It clearly distinguishes itself from sibling tools by explaining that it supplies the IDs those tools require as input, and even states 'Use FIRST whenever you have a name but need an ID.' This is far beyond a tautology and leaves no doubt what the tool does.

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 tells the agent when to use the tool ('Use FIRST whenever you have a name but need an ID') and gives detailed input guidance, such as passing the entity name only and avoiding trailing security-class words. However, it does not name alternative tools to use instead in other scenarios, so it lacks explicit when-not-to-use guidance.

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