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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description details internal behavior: it cascades through multiple lookup endpoints, labels identifiers by source, explicitly reports unresolved fields, and handles multi-match cases by returning candidates. It also discloses graceful degradation when third-party sources are unavailable. These are substantial behavioral insights not captured by 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 description is long but every sentence earns its place. It opens with concrete query examples, then proceeds logically through supported types, edge cases, and fallbacks. The structure is front-loaded with the purpose and usage instruction, and complex details are grouped by entity type. No fluff or tautology; it reads like a well-edited reference.

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 and only two params, the description is remarkably complete. It covers input formats, output behavior (figi_candidates, unresolved identifiers), source provenance, fallback behavior, and even specific examples for non-US issuers. 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?

Even though the schema covers both parameters 100%, the description adds critical semantics for the value parameter: it warns against including trailing security-class words (e.g., “revenue bonds”) and instructs to pass the issuer name exactly as printed. For the type parameter, it elaborates on how company input accepts ticker, CIK, ISIN, or name, and explains ISIN-to-LEI resolution. This goes well beyond the schema's basic descriptions.

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 immediately states the core purpose with concrete example queries (“What's the ticker for…”, “find the CIK for…”) and explicitly says it resolves a user-spoken name to identifiers other tools require. It distinguishes itself from siblings like entity_profile and compare_entities by framing itself as the prerequisite identity lookup, and even notes it handles non-equity instruments that lack tickers, which clarifies its unique scope.

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 gives an explicit directive: “Use FIRST whenever you have a name but need an ID.” It also explains when not to assert a result (when multiple instruments match, it returns figi_candidates) and how to handle degradation (if GLEIF/OpenFIGI is down, EDGAR identifiers still return). This provides clear selection criteria against the many research-oriented sibling tools.

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