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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description goes beyond this by disclosing graceful degradation (if GLEIF/OpenFIGI unavailable, EDGAR identifiers still return), handling of ambiguous matches (returns `figi_candidates` rather than asserting), and explicit `unresolved` field for unresolvable identifiers. It also mentions internal cascading through multiple endpoints, giving agents insight into performance and 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 very long and dense, but every sentence adds value — examples, edge cases, and degradation behavior are all useful. It is front-loaded with example queries, which helps agents immediately recognize the tool's purpose. However, the single-wall-of-text format could be better structured (e.g., bullet points for types and behaviors), so it's slightly less concise than ideal.

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 identifier sources, handling of bonds, drugs, and network failures), the description is remarkably complete. It covers input formats, example queries, output expectations (identifiers, candidates, unresolved), and graceful degradation. There is no output schema, so the description adequately describes what the agent can expect to receive. All edge cases an agent might encounter are explained.

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 input schema already has descriptions for both parameters, but the tool description significantly enriches them. For `value`, it provides concrete examples (AAPL, CIK format, bond issuer names) and warns against including trailing security-class words. For `type`, it explains the cross-source identity spine and drug-specific resolution. This adds meaning beyond the 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 clearly states the tool resolves user-spoken names to canonical identifiers, with specific verbs ('resolve', 'look up') and the resource ('entity name to identifiers'). It includes numerous concrete examples (ticker, CIK, LEI, RxCUI) and explains the distinction between company and drug types, making it unambiguous. It also differentiates itself from sibling tools by focusing on name-to-ID conversion rather than profile retrieval or comparison.

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 instructs 'Use FIRST whenever you have a name but need an ID', giving clear when-to-use guidance. It also explains that the tool replaces 2-3 manual lookups, implying when it is appropriate compared to alternatives. While it doesn't name specific sibling tools, it provides enough context for an agent to know it's the primary name-resolution tool, and its scope (company/drug) distinguishes it from other tools.

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