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

Annotations already declare readOnly/openWorld/idempotent/non-destructive, so the safety profile is covered. The description adds rich behavioral context: the dual structured/grounded pipeline, the enumeration of verdict values, and crucially the distinction between could_not_verify (check did not happen) and unsupported (no source covers it), plus the warning not to treat could_not_verify as evidence. This is exactly the kind of edge-case transparency that prevents agent misuse.

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 highly information-dense and logically ordered: trigger phrases, purpose, routing, return contract, error semantics, and a performance claim. It front-loads the trigger examples and ends with a time-saving note. While not terse, every sentence carries meaningful content and the structure aids parsing.

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 no output schema, the description fully specifies the return contract (verdict strings, actual value with citation, reasoning) and explains all non-obvious verdict values. It also describes the underlying data sources (SEC EDGAR + XBRL, grounded pipeline) and covers failure modes. Given the tool's complexity, this is arguably complete without needing an output schema.

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% with both parameters documented. The description goes further by explaining tolerance_pct's override semantics, its default behavior ('implied by wording, capped at 5'), and a concrete use case ('set 1–2 for hallucination detection'). This adds real guidance beyond the schema's basic field description.

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 trigger phrases ('Is it true that…' / 'fact check') and states a sharp purpose: natural-language claim verification against authoritative sources. It clearly distinguishes from siblings by positioning itself as the direct fact-checking tool and 'Replaces 4–6 sequential calls', which differentiates it from multi-step research tools like deep_research or ask_pipeworx_grounded.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the routing for company-financial vs. other claims. It lacks an explicit 'when not to use' or named alternative tools, but the use case is unambiguous enough for an agent to select it appropriately.

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