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

Annotations already cover read-only, idempotent, open-world, and non-destructive. The description adds significant behavioral context: it cascades through multiple endpoints, degrades gracefully (EDGAR returns even if GLEIF/OpenFIGI fail), labels identifiers with source, and explicitly lists unresolved identifiers rather than omitting them. It also explains the candidates behavior for ambiguous matches. This goes well beyond the 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 carries useful detail necessary for a tool with two types and multiple sources. It front-loads the purpose and usage, then goes into specifics. A bit verbose in the company type section, but not wasteful. It is structured with em-dash asides that could be trimmed, yet remains scannable. Overall appropriately sized for the complexity.

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 lacking an output schema, the description fully explains what the tool returns: identifiers with source labels, an `unresolved` field, and `figi_candidates` when ambiguous. It covers both entity types, input formats, and caveats. An agent gets enough information to call the tool correctly and interpret results. No critical gaps.

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?

Schema covers both parameters, and the description enriches them with concrete examples and edge-case rules. For `value`, it explains ticker vs CIK vs name for company and brand/generic for drug, and gives a specific instruction for bonds (pass issuer exactly as printed, not the full noun phrase). This adds practical meaning beyond the schema's brief 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 states a clear purpose: resolve a user-spoken name to canonical identifiers. It provides example queries ('ticker for', 'CIK for', 'LEI for') and explicitly contrasts with other tools by saying 'Use FIRST whenever you have a name but need an ID.' It also distinguishes between company and drug types and highlights edge cases (e.g., bonds). This makes it unambiguous and distinguishes it from siblings like compare_entities or entity_profile.

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 explicit when-to-use guidance: 'Use FIRST whenever you have a name but need an ID.' It lists example triggers and states what not to pass (e.g., not the full noun phrase for bonds). It also explains when it defers (e.g., when a name matches multiple instruments, it returns candidates rather than asserting). This is strong, actionable guidance that differentiates 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.