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

The description goes well beyond the annotations by disclosing graceful degradation ("LEI/FIGI enrichment degrades gracefully"), ambiguity handling (returns `figi_candidates` rather than asserting), and explicit reporting of unresolved identifiers under `unresolved`. It also reveals internal cascading behavior across multiple lookup endpoints, which explains why the tool might take longer or return richer results than a simple lookup.

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, filled with parentheticals and embedded details, but it is organized into clear sections (examples, use-first instruction, supported types) and almost every clause adds operational value. It is front-loaded with the core purpose and usage guidance before diving into edge cases, which helps agents quickly grasp the tool's role without reading everything.

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 having no output schema, the description covers the return values for each type, the behavior on ambiguous matches, unresolved identifiers, enrichment fallbacks, and accepted input formats. For a tool with two complex entity types and a cascading internal pipeline, the description provides enough context to call it correctly and interpret results without needing additional documentation.

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 to both parameters. For `type`, it details the supported enum values and exactly what identifiers each returns. For `value`, it gives concrete examples (AAPL, 0000320193, "ozempic") and an important anti-example warning against passing full noun phrases for bonds, which materially improves correct invocation.

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 example user phrasings ("What's the ticker for…" / "find the CIK for…") and immediately states the verb and resource: resolve a user-spoken NAME to canonical/official identifiers. It clearly differentiates from siblings by framing the tool as the input step for other tools, and explicitly enumerates supported entity types (company, drug) and which identifiers each returns.

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 when-to-use directive: "Use FIRST whenever you have a name but need an ID." It also provides context by mentioning that the tool replaces 2-3 manual lookups, which signals it is the preferred entry point for name-to-ID tasks. It does not name specific alternative tools for when not to use it, but the guidance is strong and unambiguous enough to route an agent correctly.

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