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

Beyond the read-only/idempotent annotations, the description discloses meaningful behaviors: graceful degradation when GLEIF/OpenFIGI is unavailable, returning figi_candidates when a name matches multiple instruments, explicitly reporting unresolved identifiers, labelling each identifier with its source, and ISIN-to-LEI mapping. None of this contradicts 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 definition is front-loaded with examples and the core 'use first' instruction, and every additional sentence provides substantive behavior. It is long and dense, making parsing harder, but for a tool with this much resolution behavior the length is largely justified.

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?

With no output schema, the description carries the full burden of explaining what is returned: CIK, ticker, company_name, LEI plus ownership, FIGI, RxCUI, ingredient, brand, and a pipeworx citation. It also covers ambiguity, unresolved identifiers, source labelling, and degradation, so an agent has enough context 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 describes both parameters, the description adds high-value usage constraints: pass only the entity name, never the question's full noun phrase, with a concrete bond example that explains why trailing security-class words fail. It also enriches the type parameter by clarifying what each type accepts (ticker/CIK/ISIN/company name versus brand/generic drug name), going well beyond the schema's one-line enum 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 opening sentence pairs a specific verb ('resolve') with a clear resource ('user-spoken NAME to canonical/official identifiers') and precedes it with concrete query examples. The supported types section distinguishes it from generic lookup tools by listing exactly what identifiers it returns for companies and drugs.

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 explicitly states 'Use FIRST whenever you have a name but need an ID,' which tells an agent when to choose this tool. It also notes that the tool replaces 2-3 manual lookups, reinforcing its role as the front-door resolver. However, it never names sibling alternatives or states when not to use it, so the guidance is strong but not exclusionary.

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