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

Discloses substantial behavior beyond the readOnly/openWorld/idempotent hints: USPTO soft-fails until PatentsView reactivates, an empty fda_products section is 'expected, not a failure', an empty section 'is a real no-data, not a bug', private companies return resolved:false with an explicit notes line rather than a bare failure, and type/value are interchangeable. These are precisely the edge cases an agent needs to avoid misreporting. 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 text is long, but the tool's complexity (eight data sources, eight return sections) justifies it, and the structure earns each sentence: query examples first, then the ALWAYS-PREFER directive, then a tree-like walk-through of sources and return fields, ending with parameter and error semantics. Slightly dense, but 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?

For a tool this complex with no output schema, the description is essentially complete. It explains every return section, the sources_used/sources_failed convention, all accepted value shapes, and both error pathways (private company, empty data). An agent armed with this text plus the schema can call the tool correctly and interpret its output accurately in every documented scenario.

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 nevertheless adds value beyond the schema: it clarifies that 'type' accepts company/ticker 'interchangeably' with identical behavior — a nuance the mutually exclusive enum does not convey — and gives concrete value examples ('AAPL', '0000320193', 'Moderna') plus the EDGAR name-resolution rule and private-company fallback. That pushes it above baseline.

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 multiple concrete query phrasings, then states a specific verb-resource pairing: 'full cross-source profile of a US public company in ONE parallel call.' It explicitly enumerates the eight fan-out sources (SEC EDGAR, XBRL, USPTO, USAspending, FDA, DOL LCA, news, GLEIF) and the exact return sections, distinguishing it from siblings like compare_entities and resolve_entity via the 'full ... profile ... in ONE parallel call' framing.

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 an explicit when-to-use directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' This tells the agent to reach for this tool over piecemeal lookups. However, it does not name the sibling tools that are NOT this tool (e.g., compare_entities for side-by-side comparison, resolve_entity for pure ticker/CIK resolution) or state when to choose those instead, leaving exclusions to inference.

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