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

The description goes well beyond the annotations, detailing exactly what identifiers are returned (CIK, ticker, LEI, FIGI, RxCUI), how ownership data is included, that ISINs resolve to legal entities, and that LEI/FIGI enrichment degrades gracefully when upstream services are unavailable. It also discloses the internal cascading lookup behavior, which is useful context for an agent deciding whether to call this 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 information-dense and front-loaded with purpose, but it is overwrought: a long list of example queries and deeply nested parentheticals make it harder to scan than necessary. Every part contributes value, but the structure is not concise enough for an agent to parse quickly.

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 lookup tool with no output schema, the description covers inputs, supported types, returned fields, edge cases like non-equity instruments, and failure degradation. The main gap is that it does not state explicitly what happens when a name cannot be resolved at all, leaving the agent to guess whether it returns null, an error, or an unresolved list.

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?

Even though schema coverage is 100%, the description adds substantial meaning by showing example values for both type and value, explaining the difference between company and drug inputs, and warning that the entity name must be passed exactly as printed without trailing security-class words. This is exactly the kind of operational guidance that prevents failed lookups.

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/official identifiers, names the supported types (company, drug), and gives concrete example queries. It distinguishes itself from sibling tools by noting that the IDs it returns are what other tools require as input and by telling the agent to use it first when a name but no ID is available.

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 explicitly says 'Use FIRST whenever you have a name but need an ID,' which gives a strong, actionable trigger condition. It also explains that it replaces 2-3 manual lookups and covers entities like bonds that other tools might miss, though it does not name specific sibling tools or provide explicit when-not-to-use scenarios.

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