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

Annotations already mark it read-only, idempotent, and non-destructive, and the description adds substantial behavioral detail beyond that: parallel fan-out across multiple sources, USPTO soft-fail behavior, expected empty FDA results for non-biologic companies, and real 'no data' semantics via sources_used/sources_failed. This gives the agent a reliable model of what the tool will and will not return.

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, but nearly every clause earns its place given the tool's multiple data sources and edge cases. It front-loads the most important instruction ('ALWAYS PREFER') and example queries before enumerating outputs. It is borderline a wall of text, so it loses one point for scannability.

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?

There is no output schema, so the description carries the full burden of explaining return values. It lists each major output field, source, expected failure mode, and the meaning of empty sections. Combined with the parameter semantics and usage guidance, nothing critical is missing for an agent to invoke this tool 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 description coverage is 100%, but the description adds meaning beyond the schema: both 'company' and 'ticker' accept the same value shapes, names resolve via SEC EDGAR, and value can be a ticker, CIK, or company name. The description also explains the resolved:false behavior for private companies, which the schema does not cover.

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 queries and states a specific verb-resource pair: producing a 'full cross-source profile of a US public company in ONE parallel call.' It clearly distinguishes itself from chained single-pack SEC/XBRL/news lookups, even if it does not name each sibling individually.

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?

It explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' which gives a clear selection rule. It also specifies accepted input shapes and what happens for private companies. It does not compare against sibling tools like resolve_entity or compare_entities, but overall usage context is strong.

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