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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses important behaviors: ambiguous company name matches return figi_candidates without asserting a single result, unresolvable identifiers are explicitly listed under unresolved rather than omitted, and LEI/FIGI enrichment degrades gracefully when upstream sources are unavailable. This is exactly the kind of behavioral context an agent needs before invoking the tool.

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 it earns its length by covering multiple identifier systems, input formats, fallback behavior, and a common failure mode. It is front-loaded with the core purpose and usage trigger, and the later detail is organized by supported entity types. The main weakness is that it reads as a dense wall of parentheticals rather than a more scannable structured format.

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 only two required parameters and no output schema, the description is remarkably complete. It specifies all accepted inputs, explains how each entity type resolves, and describes ambiguity and failure behavior well enough for an agent to call it correctly and interpret non-obvious results. Nothing essential for selection or invocation appears to be missing.

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 schema coverage is 100%, the description adds substantial meaning: it gives concrete value examples for both types, explains what form 'value' takes for companies (ticker, CIK, or name), clarifies brand vs generic drug names, and warns against passing full noun phrases like 'NEW YORK ST DORM AUTH revenue bonds'. This goes well beyond the schema's short 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 opens with concrete user-phrase examples and then states the core action: resolving a user-spoken name to canonical/official identifiers that other tools require as input. It names specific identifier types (CIK, ticker, LEI, FIGI, RxCUI) and supported entity types, making the tool's purpose unmistakable and distinguishable from generic lookup or profile tools.

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 that this tool replaces 2-3 manual lookups, which clarifies its role in a multi-step workflow. However, it does not name sibling tools as alternatives or state explicit when-not-to-use scenarios, so it stops short of full alternative routing.

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