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

The description goes far beyond the annotations by explaining important behavioral nuances: the verdict list, the distinction between 'could_not_verify' (a failed check, not evidence) and 'unsupported' (no source covers it), the fast-path for SEC EDGAR financial claims, and the grounded pipeline for other claims. It also notes the citation/reasoning output, which is not present in the output schema.

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 the extra length is justified by the tool's complexity and the absence of an output schema. It is front-loaded with trigger phrases and purpose, then structured into routing, return semantics, and caller warnings. Some redundancy exists (multiple paraphrases for 'fact check'), but each section earns its place.

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?

Despite having no output schema, the description fully explains what the tool returns: a verdict, the actual value with a pipeworx:// citation, and reasoning. It also covers error semantics ('could_not_verify' vs 'unsupported') and the two execution paths, making the tool's behavior sufficiently clear for an agent to select and invoke it 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?

Schema coverage is 100%, so the baseline is 3, but the description adds valuable meaning beyond the schema. It gives natural-language examples for 'claim', and for 'tolerance_pct' it explains that setting 1–2 is useful for hallucination detection where material errors must be refuted, which is more specific than the schema's default 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 clearly identifies the tool as a natural-language claim verification service with a specific verb+resource: 'validate claim' / 'fact check'. It distinguishes itself from sibling research tools by focusing on factual correctness checks that return verdicts, and even mentions replacing multi-step sequential calls.

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 explicit when-to-use guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides routing guidance for company-financial claims vs. any other factual claims, and states that the tool replaces 4–6 sequential calls, effectively directing the agent away from a slower alternative pipeline.

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