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

Annotations already indicate readOnly, idempotent, openWorld, and non-destructive behavior, and the description adds substantial behavioral context: it fans out across SEC, XBRL, USPTO, USAspending, FDA Purple Book, DOL LCA, news, and GLEIF; it discloses the USPTO PatentsView API sunset in May 2025 with soft-failure; it explains sources_used/sources_failed semantics; and it notes that an empty section is real 'no data', not a bug. This far exceeds what annotations alone convey.

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 front-loaded with real user phrases and the key usage preference, which is ideal. It is long because it enumerates many return sections and failure modes, but each detail serves the agent's decision-making. It could be tightened slightly, but the length is largely justified by the tool's complexity.

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 present, the description carries the full burden of explaining return values, and it does so exhaustively: it lists every returned field, provides URI formats for filings, describes fallback behavior from GDELT to GNews, states what a private-company result looks like, and explains source-level failure reporting. An agent has enough context to invoke the tool correctly and interpret its 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 schema already documents both parameters. The description adds meaningful nuance beyond the schema by explaining that 'type' accepts 'company' or 'ticker' interchangeably, that value may be a ticker, zero-padded CIK, or company name, and that names resolve via SEC EDGAR's company-name match. These examples and clarifications help disambiguate expected inputs without being redundant.

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 produces a 'full cross-source profile of a US public company' from multiple sources in one call. It uses concrete user phrasings ('Tell me about X', 'research Acme', 'brief me on Tesla') to signal intent, and it distinguishes itself from 'chaining single-pack SEC/XBRL/news lookups', so an agent can tell it apart from narrower lookup 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 contains an explicit usage directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also clarifies boundary cases such as private companies returning resolved:false with a notes line rather than a failure, and expected empty sections for companies without certain data types. This gives clear when-to-use and when-not-to-worry guidance.

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