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

Annotations already declare read-only, idempotent, and non-destructive behavior, and the description adds significant context: the full verdict set, the critical meaning of could_not_verify (not evidence, not a refutation), unsupported semantics, and the exact percent-delta math for financial claims. This goes well beyond the annotations and greatly helps callers interpret results correctly.

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 fairly long but every sentence earns its place, covering trigger phrases, usage context, routing logic, return contract, error handling, and efficiency gains. It is front-loaded with natural language triggers and the core use case. The only slight redundancy is the final 'replaces 4–6 sequential calls' note, but it underscores the tool's value.

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 provided, the description carries the full burden of explaining return values, and it does so thoroughly: verdict types, grounded/structured actual value with citations, reasoning, and error metadata. It also covers edge cases (could_not_verify vs. unsupported) and the two distinct pipelines. This is a complete and highly actionable description for a complex tool.

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 input schema already covers 100% of parameters with detailed descriptions, including tolerance_pct's behavior and examples. The description adds some context about the exact percent-delta math and fallback routing but does not materially alter or add to the parameter semantics beyond what the schema provides, so the baseline of 3 is appropriate.

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 defines the tool as natural-language claim verification against authoritative sources, with explicit trigger phrases like 'fact check' and 'verify the claim that…'. It distinguishes itself from sibling tools like ask_pipeworx_grounded by focusing on validating factual claims and replacing multi-step pipelines, making the purpose unmistakable.

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 explicitly states when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the routing for company-financial claims vs. other claims. However, it does not name alternative tools to use instead (when-not), which is a minor gap for full usage guidance.

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