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

Annotations already declare readOnlyHint and idempotentHint, so safety is known. The description adds valuable behavioral context: parallel fan-out across multiple sources (SEC EDGAR, XBRL, USPTO, news, GLEIF), specific returned fields, a soft-fail for the patents API sunset, and a GDELT→GNews fallback. This goes well beyond the annotations and helps the agent set expectations for failure modes.

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 dense with useful information: usage triggers, alternatives, sources, return fields, parameter examples, and fallback behavior. It is structured as a single flowing paragraph that front-loads the key 'what' and 'when to use' message. While it is long, it earns its length given the tool's complexity; the em-dash string of examples is particularly effective.

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?

Given the tool has only 2 parameters and no output schema, the description carries the full burden of explaining return values. It enumerates the exact result payload (cik, company_name, recent_filings with URIs, fundamentals, patents, news mentions, LEI), and even notes the filing count and sort order. The only minor omission is explicit error behavior for invalid tickers, but the guidance to use resolve_entity mitigates that.

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 a baseline of 3 applies. The description restates the ticker/CIK format and name limitation, which reinforces the schema but adds little new meaning. It gives concrete examples ('AAPL', '0000320193') which are helpful for formatting, but the semantic burden is already fully carried by the input schema.

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 natural language triggers ('Tell me about X' / 'research Acme') and states the exact result: 'full cross-source profile of a US public company in ONE parallel call.' It clearly differentiates from siblings like deep_research, compare_entities, and resolve_entity, making the tool's scope unmistakable.

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?

Explicit guidance is provided: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' It also names the sibling alternative 'resolve_entity' for name-based inputs, and warns that names are not supported. This directly tells the agent when to choose this tool and when to reach for another.

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