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

Even though annotations already mark the call read-only and idempotent, the description reveals important runtime behavior: parallel fan-out across many sources, soft-failures (USPTO sunset), fallback ordering (GDELT→GNews), sources_used/sources_failed semantics, and resolved:false for private companies. This goes well beyond what annotations convey.

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 and dense, but it is front-loaded with examples and the ALWAYS PREFER guidance, and nearly every clause supplies operational detail an agent needs. The formatting is a single thick paragraph, which slightly reduces scannability, so it is not a 5.

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 documenting return fields, and it does so thoroughly: cik, filings URIs, fundamentals, patents, contracts, FDA products, hiring, news fallback, LEI, and sources_used/sources_failed. Edge cases like private companies and expected empty FDA sections are also covered.

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 the schema already documents type and value; the description adds substantial extra meaning with accepted shapes (ticker, zero-padded CIK, company name), interchangeability of 'company' and 'ticker', name resolution via SEC EDGAR, and illustrative examples. It doesn't reach 5 because it is mostly elaborating on schema-documented params rather than revealing completely new dimensions.

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 clearly defines the tool's job: creating a full cross-source profile of a US public company in one parallel call, and it uses concrete user-phrase examples to make intent unmistakable. It stops short of explicitly differentiating itself from close siblings such as compare_entities or resolve_entity, so it doesn't earn the top sibling-discrimination score.

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 an explicit when-to-use instruction ('ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view') and an explicit alternative to avoid. It also tells the agent which input forms are accepted, what happens for private companies, and which empty results are expected rather than failures.

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