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

The description goes far beyond the readOnly/openWorld/idempotent annotations. It discloses the full fan-out across sources, the meaning of sources_used/sources_failed, soft-fail behavior for patents, expected empty sections as real 'no data', resolved:false behavior for private companies, and fallback logic for news. This is rich, accurate behavioral context.

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 densely informative; nearly every clause earns its place by explaining input variants, output fields, source behavior, or failure semantics. It is front-loaded with user-intent examples and the key preference rule. It could be improved with structured bullets or clearer section breaks, but there is no filler.

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 fully explains what the tool returns: CIK, company_name, resolved_from/to, recent_filings with URIs, key fundamentals, per-source data, and sources_used/sources_failed. It also clarifies input resolution, private-company behavior, and type/value interchangeability, so an agent has everything needed to invoke and interpret results correctly.

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 schema already documents that type accepts company/ticker interchangeably and that value can be a ticker, CIK, or company name. The description reinforces this with concrete examples like 'AAPL', '0000320193', and 'Moderna', but does not add significant semantic meaning beyond the schema.

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 identifies the tool's purpose: producing a full cross-source profile of a US public company in one call. It uses specific verbs ('profile', 'brief me', 'research') and a specific resource type, and it distinguishes itself from chained single-purpose lookups, making it unambiguous even among sibling 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 explicit guidance: ALWAYS PREFER this over chaining single-pack lookups when the user wants a holistic view. It also explains what happens for private companies and what input forms are accepted. However, it does not explicitly name sibling alternatives like compare_entities or resolve_entity or state when those should be preferred instead.

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