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

Even though annotations already declare read-only, open-world, and idempotent behavior, the description adds valuable runtime context: it fans out across multiple sources, returns sources_used/sources_failed, treats empty sections as real 'no data' rather than bugs, and returns resolved:false with an explicit notes line for private companies. This goes well beyond the 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 description is long, but it is front-loaded with trigger examples and the key 'ALWAYS PREFER' directive before diving into sources and return fields. The dense semicolon-separated enumeration is structured and mostly high-value, though some content lightly repeats what the schema already states.

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 values, and it does so thoroughly: it lists the main result fields, notes the sources fanned out, explains sources_used/sources_failed semantics, and covers both successful and private-company failure behavior. An agent has enough to select and invoke the tool correctly.

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 schema already covers both parameters fully, so the baseline is 3, but the description adds meaningful edge-case semantics: zero-padded CIK formats, name resolution via SEC EDGAR, private-company fallback behavior, and the fact that type and value shapes remain interchangeable regardless of which enum value is chosen. This helps an agent construct valid inputs.

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 phrasings and states that this tool produces a 'full cross-source profile of a US public company in ONE parallel call.' It clearly identifies the resource and explicitly distinguishes itself from chaining single-pack SEC/XBRL/news lookups, so an agent can tell it apart from related lookups.

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 gives explicit usage guidance: prefer this tool 'when the user asks for a holistic view' and 'ALWAYS PREFER over chaining single-pack lookups.' It does not name specific sibling alternatives such as search or resolve_entity, but the holistic-view trigger and the strong preference statement provide clear selection context.

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