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

Beyond the annotations, the description discloses substantial behavior: ambiguous matches return figi_candidates without asserting a result, unresolved identifiers are explicitly listed under unresolved, LEI/FIGI enrichment degrades gracefully if upstream APIs are unavailable, and every identifier is labeled with its source. This is exactly the kind of behavioral transparency that helps an agent predict tool outcomes.

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 nearly every sentence carries useful operational guidance or an edge-case warning. It is front-loaded with the purpose and the critical 'Use FIRST' directive. The structure could be cleaner — the company sub-description is a large parenthetical run-on — yet it remains appropriately detailed for a tool with this much behavioral nuance.

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, the description is remarkably complete: it covers input forms, supported entity types, return-behavior quirks (figi_candidates, unresolved), data sources, fallback behavior, and cross-tool value. An agent has enough context to call the tool correctly and interpret results without further manual discovery.

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 schema already covers the value parameter thoroughly, including input formats and the bond issuer-name warning. The description adds meaningful parameter-level semantics by explaining what each supported type resolves to: company returns CIK, ticker, company_name, LEI, ownership, and FIGI; drug returns RxCUI, ingredient, and brand. This goes beyond the schema's bare enum, though much of the value-parameter guidance is duplicated from the schema rather than newly added.

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 function: resolving a user-spoken name to canonical identifiers that other tools require as input, reinforced by multiple concrete examples ('What's the ticker for...', 'find the CIK for...'). It also enumerates the supported entity types and, by framing the tool as a name-to-ID resolver, distinguishes it from sibling tools like entity_profile or compare_entities.

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 explicit usage guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains that the tool replaces 2-3 manual lookups, which helps an agent understand its place in the pipeline. However, it does not explicitly state when not to use it or name alternative sibling tools for already-resolved entities or profile retrieval, so it falls short of full exclusion guidance.

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