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

The description goes far beyond the annotations by enumerating the data sources fanned out across (SEC EDGAR, XBRL, USPTO, USAspending, Purple Book, DOL LCA, news, GLEIF), explaining that empty sections mean real 'no data' not bugs, and documenting name resolution behavior plus the private-company resolved:false outcome. It also clarifies that type is interchangeable, preventing a common misuse. This is rich, actionable behavioral context.

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 but front-loaded with example queries and the core directive. Every section earns its place—usage, fan-out scope, input semantics, and edge cases. The punctuation is somewhat erratic (broken formatting marks like 'GLEIF,' and 'DELT→GNews'), which slightly reduces polish, but it remains information-dense without redundancy.

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?

For a tool with only two required string parameters and rich annotations (read-only, open-world, idempotent, non-destructive), the description fully covers input variants, output semantics, source list, success/failure signals, and expectations for private companies. There is no output schema, so the description carries that burden and handles it well.

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%, but the description adds meaning beyond the schema by clarifying both parameters can carry the same value shapes and by explicitly stating type 'company' or 'ticker' behave identically. The description also gives examples and explains the resolved_from/resolved_to behavior for name inputs, so the agent understands the semantic equivalence and edge cases beyond the raw enum/string definitions.

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 is explicit about what the tool does: it returns a full cross-source profile of a US public company in one parallel call, with concrete example queries. It names the resource (a company) and the verb (profile/research/brief) and distinguishes itself from chained single-pack lookups. Even without the sibling list, the intent is unmistakable.

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 explicitly says ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view, which is strong when-to-use guidance. It also names the input alternatives (ticker, CIK, company name) and the failure mode for private companies, so an agent knows what to expect and when to choose another tool.

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