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

The description goes well beyond the readOnly/idempotent annotations by disclosing real behavioral details: USPTO "soft-fails until reactivated," "an empty section is a real 'no data', not a bug," and private-company inputs yield resolved:false with an explicit notes line. It also explains the sources_used/sources_failed mechanism so the agent can interpret partial results.

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 but front-loaded with usage examples and the core definition, then moves into sources, output semantics, and edge cases. Every sentence carries operational information, though it repeats some schema details (e.g., valid value formats), preventing a perfect score.

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?

Because there is no output schema, the description must explain return values, and it does: it enumerates output fields, the resolved_from/resolved_to behavior, the sources_used/sources_failed indicators, and expected empty/soft-fail cases. For a complex multi-source tool, this is sufficient for an agent to invoke it correctly and interpret 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?

The input schema already covers 100% of parameters, and its descriptions already explain that both type values behave identically and that value can be a ticker, CIK, or name. The long description mostly restates these accepted shapes with examples ("AAPL", "0000320193", "Moderna") without adding materially new parameter-level meaning, so it stays at the schema-covered 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 concrete user intents ("Tell me about X", "research Acme", "brief me on Tesla") and then defines the tool as a "full cross-source profile of a US public company in ONE parallel call." It lists the specific output sections (CIK, filings, fundamentals, sources_used/failed), making the tool's purpose and scope unmistakable relative to generic research 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 states "ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view," giving a clear routing rule. The example phrasings also act as triggers, and the note about private companies returning resolved:false tells the agent what happens in the non-public-company case.

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