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

Annotations already declare readOnly/idempotent/non-destructive, and the description goes well beyond them: it discloses the parallel fan-out across ten sources, defines sources_used/sources_failed semantics ('an empty section is a real no data, not a bug'), explains the resolved:false private-company path, calls out the FDA section exception for small-molecule/generic drugs, and documents the GDELT→GNews fallback chain. 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?

Front-loaded with the core purpose and example queries, and every sentence carries real information. However, the entire description is one dense run-on paragraph with heavy em-dash parenthetical stacking and crammed source lists ('patents (USPTO)', 'federal contracts (USAspending)'), which significantly hurts scanability for an agent trying to extract key facts quickly.

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?

For a tool with no output schema and enormous surface area — ten data sources, URI formats, resolution behavior, failure modes, and type semantics — the description is remarkably thorough. It enumerates the returned sections, gives the filing URI scheme, defines LATEST 10-K semantics, and teaches how to interpret empty sections. Minor omissions like exact output key names are acceptable given the breadth covered.

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 meaningful value beyond the schema: the zero-padded CIK convention ('0000320193'), name resolution via SEC EDGAR company-name match, the resolved_from/resolved_to behavior when a name is passed, and the explicit statement that type='company' and type='ticker' accept identical value shapes.

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?

States the verb-resource pair directly: 'full cross-source profile of a US public company in ONE parallel call,' backed by concrete example queries ('brief me on Tesla', 'everything you know about $TICKER'). The 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups' line explicitly separates it from piecemeal lookup 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?

Gives explicit when-to-use guidance: prefer this tool whenever the user asks for a holistic view, and prefer it over chaining single-pack SEC/XBRL/news lookups. It also defines the failure context — a private company returns resolved:false with a notes line rather than a bare failure, which tells the agent what to expect in that case. It doesn't name specific sibling tools as alternatives, but it defines the alternative pattern clearly.

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