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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses rich behavioral context: results fan out in ONE parallel call, empty sections are 'a real no data, not a bug', `sources_used`/`sources_failed` are returned, GDELT→GNews fallback behavior, resolved_from/resolved_to for name inputs, and the explicit note that small-molecule/generic drugs showing no FDA products is expected, not a failure. These are exactly the edge cases that cause agents to misreport failures, and they go far 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?

Every sentence carries real information with no filler, and the intent is front-loaded with example queries. However, the entire description is one dense unbroken wall of text with no section breaks or bullets, and six nearly identical example phrasings could be trimmed to two. For an agent parsing this, the information is all valuable but the structure makes it harder to scan than it should be.

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?

This is a highly complex tool with no output schema, yet the description covers inputs, fan-out sources, per-section return contents, failure semantics, fallback chains, and expected-empty cases. The description even tells the agent how to differentiate a genuine 'no data' from a bug — the single most important non-obvious detail. For a tool of this complexity with no output schema, nothing essential is missing for correct invocation and result interpretation.

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 already describes both parameters well, establishing a baseline of 3. The description adds value beyond the schema with concrete examples ('AAPL', '0000320193', 'Moderna'), the explicit statement that type is interchangeable, the SEC EDGAR name-resolution behavior, and the resolved:false semantics for private companies. This is meaningful supplementary meaning, not mere restatement.

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 queries then states the exact function: 'full cross-source profile of a US public company in ONE parallel call.' It names the fan-out sources (SEC EDGAR, XBRL, USPTO, USAspending, Purple Book, DOL LCA, GDELT, GLEIF) and the returned sections, which makes it easy to distinguish from siblings like resolve_entity or compare_entities. The verb-resource-scope triple is fully specified.

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?

There is an explicit directive: 'ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view,' which clearly states when to use this tool and what to prefer it over. It also defines accepted input forms and what happens for private companies. However, it never names specific sibling alternatives (e.g., resolve_entity for identifier-only needs, compare_entities for comparisons) or states explicit when-not-to-use conditions, leaving a small gap.

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