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

The description goes far beyond the readOnly/idempotent annotations by explaining cross-source fan-out, the sources_used/sources_failed mechanism, the meaning of empty sections, private-company resolved:false behavior, and the interchangeability of type values. It also notes expected edge cases like small-molecule drugs producing no FDA product results, which is useful and non-obvious.

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 content is highly informative and front-loaded with usage examples, but it is delivered as one long, dense wall of text with heavy parentheticals and semicolon chains. Some details, such as accepted value formats and type interchangeability, are repeated from the schema. Breaking it into structured bullets or shorter paragraphs would improve scanability while keeping the same value.

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, the description carries the full burden of explaining return shape, and it does so comprehensively: top-level fields, per-section specifics, source success/failure reporting, and edge-case resolutions. An agent has enough context to invoke the tool correctly and interpret non-obvious outcomes like empty sections or unresolved private companies.

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?

Even though the input schema already covers both parameters at 100%, the description adds practical meaning: ticker/CIK/name examples, the fact that names resolve via SEC EDGAR, the private-company fallback behavior, and the clarification that 'company' and 'ticker' types are interchangeable. This exceeds what the schema alone communicates.

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 identifies a specific verb-equivalent activity — producing a full cross-source profile of a US public company — and clearly distinguishes this from single-pack SEC/XBRL/news lookups. The example trigger phrases and the explicit 'ALWAYS PREFER over chaining' make its role unmistakable relative to sibling 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 states exactly when to use the tool: when the user asks for a holistic view like 'tell me about X', 'research Acme', or 'brief me on Tesla'. It also explicitly discourages chaining single-pack lookups in favor of this one-call approach and explains the accepted input forms, which is strong routing guidance.

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