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

The description goes well beyond the readOnly/openWorld/idempotent annotations by detailing the two execution paths, the specific verdict enums, and the critical error semantics: 'could_not_verify means the check did not happen… it is NOT evidence for or against the claim, and must not be shown as one.' It also notes the composite nature (replaces 4–6 sequential calls). This is rich 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 long but well-structured: it opens with trigger phrases, states the core function, then explains routing, return values, and a critical error caveat. Every major sentence contributes new information; minor redundancy with the schema exists but is not excessive.

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?

With no output schema, the description compensates by explaining the verdict list, the grounded/structured value with pipeworx:// citation, reasoning, and the special meanings of could_not_verify and unsupported. It also covers both claim categories and the composite nature, making it complete for the tool's complexity.

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?

Both parameters are fully described in the schema (100% coverage), so the baseline is 3. However, the description adds meaningful usage nuance: tolerance_pct overrides the implied default, is capped at 5, and should be set to 1–2 for hallucination detection. The example claims also aid understanding, elevating the value beyond the schema.

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 clearly states the tool's purpose: 'natural-language claim verification against authoritative sources' and 'check whether something a user said is factually correct.' It distinguishes itself from siblings by outlining the two routing paths (SEC EDGAR for financial claims vs. grounded pipeline for other claims), making its scope unambiguous.

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 provides explicit usage triggers like 'fact check' and 'verify the claim that…' and states 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between financial and non-financial claims, but it does not explicitly mention alternative tools or when not to use this tool, keeping it from a perfect score.

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