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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: graceful degradation if GLEIF/OpenFIGI are unavailable, explicit labeling of sources, and the statement that unresolved identifiers appear under 'unresolved' rather than being omitted. It also discloses internal cascading lookups, giving the agent a realistic expectation of cost 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 long and dense but well-structured: front-loaded with examples and a 'Use FIRST' directive, followed by supported types and behavioral notes. Some redundancy exists (e.g., 'Use FIRST' appears twice), but the length is justified by the tool's complexity and multiple entity types. It remains easily scannable.

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 and lack of output schema, the description is remarkably complete. It details return contents for both company (CIK, ticker, company_name, LEI, FIGI, ownership, unresolved identifiers) and drug (RxCUI, ingredient, brand, citation), fallback behavior, and input variants. This equips the agent to understand results and decide when to use the tool.

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 covers both parameters (type, value) with descriptions, giving 100% coverage. The description further enriches semantics by explaining how ISIN maps to legal entity via GLEIF, examples for ticker/CIK/name, and the drug type details (RxCUI, ingredient, brand). This goes well beyond the schema's basic field 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 clearly states the tool resolves user-spoken names to canonical identifiers, with a specific verb ('resolve') and resource ('a user-spoken NAME to the canonical/official identifiers other tools require as input'). It distinguishes itself from siblings like search or entity_profile by emphasizing it produces IDs needed by other tools, and it provides concrete examples for multiple entity types.

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?

Explicit guidance: 'Use FIRST whenever you have a name but need an ID.' This directly tells the agent when to invoke the tool. It also mentions that it replaces 2-3 manual lookups, reinforcing its role. While it doesn't list exclusions, the 'FIRST' directive and specific supported types provide clear usage context.

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