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

Beyond the readOnly/idempotent annotations, it discloses parallel fan-out across named sources, a GDELT→GNews fallback, sources_used/sources_failed semantics, that empty sections mean real absence of data, and that private companies resolve to resolved:false with a notes line. This gives the agent valuable behavioral expectations that annotations alone do not.

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 and long, but the purpose and usage rule are front-loaded and every sentence carries a distinct constraint or behavior. It could be tightened with bullets, but the length is justified by the number of data sources and edge cases covered.

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 return-format burden and does so thoroughly: it names each returned section, specifies the 10-K fields, gives URI patterns, explains fallback behavior, and states how to interpret empty sections and failed resolution. This is sufficient for an agent to call the tool correctly for a wide range of inputs.

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% and both parameters are already documented with the same examples and accepted value shapes (ticker, zero-padded CIK, or name), so the description mostly restates the schema. It adds contextual details like resolved_from/resolved_to and private-company behavior, but these are output semantics rather than new parameter semantics.

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 and states the deliverable: 'full cross-source profile of a US public company in ONE parallel call.' It also differentiates itself from single-pack SEC/XBRL/news lookups, so an agent can distinguish it from the sibling lookup 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?

It explicitly says 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view' and gives example queries. It also clarifies the acceptable inputs and the private-company edge case, so the agent knows when the tool is appropriate and what to expect outside its intended scope.

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