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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses several important behaviors: graceful degradation when enrichment sources are unavailable, ambiguous matches returning `figi_candidates` instead of asserting a single answer, explicit reporting of unresolved identifiers under `unresolved`, and the internal cascading across multiple endpoints. These details go well beyond what annotations or schema 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 long but densely packed with useful information; no sentence is filler. It is front-loaded with query examples and the core directive. However, it could be better structured (e.g., with paragraphs or bullet points) to improve scannability. Given the tool's complexity, the length is justified but slightly exceeds what is strictly 'concise'.

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?

Despite having no output schema, the description thoroughly explains what the tool returns (CIK, ticker, LEI, FIGI, RxCUI, and behavior like `figi_candidates` and `unresolved`). It also details input handling, degradation, and supported types. For a tool with two parameters and rich behavior, an agent has everything needed to invoke 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?

Although the schema already documents both parameters (100% coverage), the description adds substantial meaning: it explains the 'type' enum semantics and gives detailed guidance for 'value', including examples, the instruction to pass the entity name only, and a critical caveat for bonds about matching instrument names rather than full noun phrases. This clarifies nuances that the schema alone cannot convey.

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 ('resolve') and resource ('a user-spoken NAME to the canonical/official identifiers'), and immediately differentiates itself from sibling tools by naming the exact identifier types it returns. The examples ('What's the ticker for…') make the purpose unmistakable, and it clearly distinguishes from entity_profile by focusing on ID resolution rather than profiling.

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', giving a direct usage trigger. It also enumerates the types of inputs accepted (ticker, CIK, ISIN, company name, drug name), which tells an agent when this tool is appropriate. However, it does not state when NOT to use it or provide alternatives (e.g., when a user wants a full profile rather than an ID), so it lacks explicit exclusionary guidance.

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