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

Annotations already declare readOnlyHint and idempotentHint. The description adds context by mentioning it cross-references HuggingFace leaderboard models (implying external calls) and that it returns confidence metrics and hallucination warnings. This goes beyond the annotations without contradicting them.

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 four sentences, each providing useful information: purpose, target audience, inputs/outputs, and external cross-referencing. It's slightly verbose but not bloated, and information is front-loaded. A 4 is appropriate.

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 a rich schema (100% coverage) and output schema present, the description covers purpose, inputs, outputs, and context (risk assessment). It doesn't need to explain return values. The only minor gap is not mentioning the async parameter behavior, but that's covered in the schema. Overall complete.

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 baseline is 3. The description adds nuance that the text input can be 'text snippets or model outputs,' which clarifies input flexibility beyond the schema's 'LLM-generated text.' This extra context justifies a 4.

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 the tool 'evaluates the likelihood of hallucination in LLM responses' and explains the method ('by comparing against HuggingFace model confidence scores'). This specific verb+resource+method distinguishes it from sibling evaluation tools like bias_amplification_tracker or jailbreak_attempt_detector.

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?

It identifies target users ('risk assessment personas') and the goal ('quantify response reliability'), and specifies accepted inputs ('text snippets or model outputs'). However, it doesn't explicitly mention when not to use the tool or name alternatives, so it falls short of a 5.

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