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

Annotations already mark the tool as read-only, idempotent, and non-destructive, so the burden on the description is lower. Even so, the description adds substantial behavioral context: soft-failure of the USPTO source, empty sections meaning genuine 'no data', private-company resolution failure with a notes field, FDA-licensed products expected to be absent for small-molecule-only companies, and the sources_used/sources_failed contract. This goes far beyond 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 description is long, but the tool is complex and has no output schema, so the length is largely justified. It front-loads example queries and the key 'ALWAYS PREFER' rule, then systematically covers sources, return fields, failure modes, and parameter shapes. A few points repeat schema information, but every sentence adds operational value.

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?

Given the tool's complexity and lack of an output schema, the description is unusually complete. It enumerates every source, the exact return fields (cik, resolved_from/resolved_to, recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI), failure semantics, and accepted value shapes. An agent has everything needed to invoke it correctly and interpret 'empty' 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?

Schema coverage is 100%, so the baseline is 3. The description adds useful semantics beyond the schema: type values are interchangeable, value can be a ticker, zero-padded CIK, or company name, and names resolve via SEC EDGAR. It also notes the zero-padding requirement and gives concrete examples, which makes parameter usage clearer without redundancy.

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 uses a specific verb+resource: it 'profiles' a 'US public company' by fanning out across multiple authoritative sources. It also distinguishes itself from simpler lookups by explicitly saying 'full cross-source profile in ONE parallel call' and 'ALWAYS PREFER over chaining single-pack ... lookups', making its scope unmistakable.

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 when-to-use guidance: use for holistic 'Tell me about X' / 'research Acme' requests and prefer it over chaining individual lookups. It does not explicitly name sibling alternatives like compare_entities or resolve_entity or state when NOT to use those, but the preference rule and the private-company/empty-section behavior provide solid operational context.

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