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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, but the description adds substantial context beyond that: it discloses multi-source fan-out behavior, lists all return fields including edge cases (e.g., 'fda_products ... only small-molecule/generic drugs will show none here, that is expected'), and explains failure modes like 'sources_used / sources_failed say which of these actually returned data' and private company handling ('returns resolved:false with an explicit notes line, not a bare failure'). No contradiction with 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 (over 300 words) but front-loaded with example prompts and a clear purpose, and every sentence adds operational detail (sources, return fields, error behavior). While it could be restructured as a bulleted list for readability, its density is justified by the tool's complexity; nothing is 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?

With no output schema, the description fully carries the burden of explaining return values. It enumerates all data sections (recent_filings, fundamentals, patents, federal_contracts, fda_products, hiring, news, LEI) and clarifies expected absences (e.g., empty fda_products for non-biologic companies). It also covers input resolution, private-company handling, and the `type` parameter's semantics, making the tool fully self-explanatory.

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% — both `type` and `value` already have detailed descriptions covering the accepted inputs and the interchangeability of 'company'/'ticker'. The tool description repeats this information (e.g., 'pass a ticker, zero-padded CIK, or a company name') without adding new semantic meaning, so it stays at the baseline for well-documented schemas.

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 it produces a 'full cross-source profile of a US public company in ONE parallel call' and gives multiple concrete example prompts (e.g., 'tell me about X', 'research Acme'). It explicitly distinguishes itself from chaining single-pack SEC/XBRL/news lookups, making the purpose unambiguous relative to siblings.

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', providing both a clear when-to-use condition and the alternative (sequential single-pack lookups). This gives a direct routing rule, comparable to the get_calls calibration example.

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