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

Beyond the readOnly/openWorld/idempotent annotations, the description adds critical behavioral nuance: it explains that 'could_not_verify' means the check did not occur and must not be treated as evidence, and it distinguishes 'unsupported' as a coverage gap. It also details the two verification paths and the return structure (verdict, value, citation, reasoning), providing rich behavioral context beyond the annotations.

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 a single dense paragraph but every sentence carries weight: trigger phrases, usage directive, routing logic, return values, and critical caveats. It is appropriately sized for the tool's complexity, though it could benefit from slight restructuring (e.g., separating the 'IMPORTANT' caveat into a distinct note) to improve scannability.

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?

Given the tool's complexity (two pipelines, multiple verdict types, error semantics), the description is exceptionally complete. It explains what the tool returns, how it handles failures (could_not_verify vs. unsupported), and why it is valuable (replaces multiple steps). No output schema exists, so the description correctly carries the full burden of explaining return values and edge cases.

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 covers 100% of the parameters with descriptions, so the baseline is 3. The description does not add parameter-specific details beyond what the schema already provides (e.g., tolerance_pct is only implied via 'percent-delta math'), and it does not explain how the 'claim' string should be formatted beyond examples in the schema. Thus it meets but does not exceed the baseline.

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 tool, with explicit trigger phrases and a specific verb+resource framing ('verify the claim'). It distinguishes itself from siblings by detailing the two distinct verification paths (structured SEC EDGAR for company financials vs. grounded pipeline for all other claims), making its purpose and scope 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 gives a direct usage directive: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the routing to different pipelines and notes that it replaces 4–6 sequential calls, implying efficiency. However, it does not explicitly name alternative tools or state when NOT to use this tool, so it falls short of the highest bar.

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