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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds substantial behavior beyond annotations: it fans out across multiple sources (SEC EDGAR, XBRL, USPTO, USAspending, Purple Book, DOL LCA, news, GLEIF), returns specific fields (cik, company_name, recent_filings, fundamentals, sources_used/sources_failed), and explicitly says 'an empty section is a real "no data", not a bug' — a critical behavioral disclosure that prevents the agent from misinterpreting empty results. It also explains the name-resolution behavior ('names now resolve via SEC EDGAR's company-name match') and the special case for private companies.

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 structured: it starts with everyday-language trigger examples, then states the tool's purpose, then lists the fan-out sources, then details return fields, then covers input formats and edge cases. It's long but every sentence carries information; the only slight ding is that source-listing and return-field-listing could be tightened. Still, the front-loading of 'ALWAYS PREFER' makes the core routing decision immediate.

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 2 parameters, 100% schema coverage, and rich annotations, the description is remarkably complete. It describes the tool's scope, trigger phrases, input shapes, resolved/unresolved outcomes, sources fanned out, return fields, and the 'no data' semantics. The only thing not covered in detail is the exact structure of the output object, but no output schema exists and the description names enough fields (cik, company_name, recent_filings, fundamentals, sources_used, sources_failed, resolved, notes) that an agent knows what to expect.

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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds value by enumerating valid input shapes ('Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna")') and clarifying that 'type' accepts 'company' or 'ticker' interchangeably — which provides extra semantic clarity beyond the schema's enum description. The 'resolved_from/resolved_to' mention also adds meaning for the value parameter that the schema alone wouldn't convey.

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 invocation patterns ('Tell me about X', 'research Acme', 'brief me on Tesla') and then states a specific verb+resource: 'full cross-source profile of a US public company in ONE parallel call.' It clearly distinguishes itself from single-pack lookups by saying 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view.' This gives the agent a precise understanding of what the tool accomplishes and how it differs from 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 explicitly says when to use this tool ('when the user asks for a holistic view') and what to prefer it over ('chaining single-pack SEC/XBRL/news lookups'). It also provides concrete routing examples for valid inputs (ticker, CIK, company name) and even describes failure modes ('a private company (no CIK/ticker) returns resolved:false with an explicit notes line, not a bare failure'). This is exemplary usage guidance.

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