Detect Hallucination Risk

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false), so the bar is lower, and the description adds real behavioral detail: the four analysis dimensions and the conditional behaviour that factual-disagreement detection only activates when `source` is present. It stops short of describing output shape, confidence levels or scoring thresholds, so it is not a 5.

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?

A single front-loaded sentence leads with the core purpose before the enumeration, and the parenthetical example is the only elaboration, placed where it disambiguates `source`. Nothing is redundant with the title or annotations.

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?

For a 3-parameter analysis tool with no output schema, the definition explains inputs well but says nothing about what comes back -- risk score, label, per-signal breakdown -- and gives no hint about candidate count expectations or latency/cost. Annotations cover safety, so the gap is in return-value and operational context rather than in purpose.

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; the description goes beyond it by giving a concrete semantics example for `source` ('the source names one day/month/number and the response names another') and by tying `candidates` to self-consistency scoring. That clarifies the optional parameters' purpose rather than merely restating their schema text.

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?

States a specific verb and resource ('Detect hallucination risk in AI-generated text') and then enumerates the four signals it analyses (hedging language, self-consistency across candidates, unsupported factual claims, source disagreement). That mechanism list uniquely identifies this tool against siblings like rai_scan or rai_trust_score without opening any schema.

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?

Usage is only implied: the description shows the conditional value of supplying a `source` ('when a `source` is supplied ... enables explicit factual-disagreement detection'), which tells the agent when that parameter matters. There is no tool-level when-to-use guidance, no mention of when to prefer rai_scan, rai_trust_score or rai_redteam_analyze, and no stated prerequisites.

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