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

The description goes well beyond the read-only/idempotent annotations by explaining the two processing paths (structured SEC EDGAR vs. grounded pipeline), the exact verdict values, and the critical distinction between could_not_verify (failure with verification_error) and unsupported (uncovered source). It also discloses that citations are returned via pipeworx:// and that the tool replaces 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?

Despite being long (~180 words), it is densely packed with information and front-loaded with trigger phrases. Every sentence earns its place: routing logic, return values, important caveats, and efficiency note. There is no filler or redundancy.

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 types, the returned citation and reasoning, and the semantics of error cases (could_not_verify vs unsupported). It also covers parameter usage and two distinct claim types. Given the tool's complexity, this is complete enough for an agent to use correctly.

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?

The schema already covers both parameters (claim, tolerance_pct) with descriptions, so baseline is 3. The description adds substantial value: concrete claim examples, explanation that tolerance_pct overrides implied tolerance, a usage hint for hallucination detection (set 1–2), and default behavior (implied by wording, capped at 5). This goes 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 opens with explicit trigger phrases ("Is it true that…", "fact check", "verify the claim that…") and states its core function: "natural-language claim verification against authoritative sources." It clearly distinguishes itself from sibling search/ask tools by framing the output as a verdict (confirmed/refuted/etc.) rather than a search result, and even explains it replaces multi-step pipelines.

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 gives a clear usage context: "Use whenever the agent needs to check whether something a user said is factually correct." It also separates company-financial claims from other claims, indicating routing. However, it does not explicitly name alternatives or provide 'when not to use' guidance (e.g., when the user asks an open-ended question without a claim to verify).

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