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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the read/idempotent profile is established. The description goes well beyond that by disclosing internal fan-out sources (SEC EDGAR, XBRL, USPTO, USAspending, Purple Book, DOL LCA, GDELT→GNews fallback, GLEIF), soft-fail behavior for USPTO mid-2025, the resolved_from/resolved_to behavior, the resolved:false + notes case for private companies, and that sources_used/sources_failed indicate actual data availability. This is exactly the behavioral context an agent needs.

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 information-dense and front-loaded with trigger phrases and the core directive. Every clause earns its place: the fan-out list, soft-fail caveat, and return-component explanation all prevent misinvocation. It loses one point because it is a single dense block rather than structured, and the extremely long parenthetical about fda_products could be compressed without much information loss, though nothing is truly fluff.

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 complex multi-source tool with 2 parameters, 100% schema coverage, no output schema, and read-only annotations, the description covers all essential invocation facts: input forms, resolution behavior, fan-out sources, result contents, failure semantics, and the empty-section caveat. There is no output schema, so the detailed listing of returned fields (cik, company_name, recent_filings with pipeworx URIs, fundamentals, sources_used/sources_failed) is necessary and present. Nothing an agent needs to invoke it correctly is missing.

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 the schema descriptions already explain type and value shapes. The description adds meaning beyond the schema by clarifying that 'company' and 'ticker' are interchangeable and both accept the same value shapes, that names resolve via SEC EDGAR's company-name match, that ticker/CIK/company name are all valid, and that a private company returns resolved:false with a notes line rather than a hard failure. This reduces the chance of an agent choosing the wrong type-value combination.

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 names a specific verb and resource — 'full cross-source profile of a US public company in ONE parallel call' — and opens with a rich set of natural-language trigger examples. It clearly differentiates from siblings: it says ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when a holistic view is requested, and the sibling list contains single-purpose tools like resolve_entity, compare_entities, and deep_research, so the agent can distinguish which tool is for comprehensive profile assembly.

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 it ('when the user asks for a holistic view'), states a directive ('ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups'), and explains what inputs are accepted (ticker, CIK, company name) and what happens for private companies. It also notes that empty sections are real 'no data', not bugs, which prevents false-negative interpretation.

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