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

With no annotations, the description carries full burden. It reveals the tool looks for factual mismatches and specific hallucination patterns, and returns evidence checks and red flags. However, it does not disclose limitations, potential false positives, or whether it is purely read-only. Some behavioral context is provided but not exhaustive.

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?

Two concise, information-dense sentences. The main action is front-loaded, and every word adds value. 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 only two simple parameters, no output schema, and no annotations, the description covers the core behavior and output shape ('evidence checks and red flags'). However, it leaves some ambiguity about what exactly an 'evidence check' or 'red flag' looks like, which might be important for an agent to interpret results 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% for both parameters ('statement' and 'context'), so the schema already explains their meaning. The description adds no additional parameter-level detail, which is acceptable given the schema's high coverage.

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 a specific action ('Compare an agent's statement against ground truth evidence') and a distinctive focus ('dangerous hallucination patterns', 'absolute language', 'suspect claims'). This differentiates it from simple verification tools like verify_claim, though it doesn't explicitly name alternatives.

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?

No guidance is provided on when to use this tool versus its sibling tools (e.g., verify_claim, provenance_trace, confidence_gate). The description implies use for hallucination detection, but does not state when to prefer it over alternatives or any exclusions.

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