check_hallucination_risk

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

With no annotations, the description carries the behavioral burden. It explains the two operating modes (grounding check vs. heuristic flagging) but does not describe the output format, side effects, or any limitations. It adds some transparency but leaves notable gaps.

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 two concise sentences, front-loaded with the primary purpose and then a clear conditional. No wasted words; every sentence earns its place.

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?

The tool has 3 parameters, no output schema, and no annotations. The description explains the tool's core behavior and the conditional use of source_text, but does not specify what the tool returns (e.g., a risk score, a flag, a report). This missing output information makes it less complete than ideal.

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 value by explaining how source_text changes the tool's behavior ('checks grounding') and implying the role of claim_count as a threshold. This goes beyond simple parameter listings.

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 estimates 'hallucination likelihood in agent output,' which is a specific verb+resource. It distinguishes from sibling tools like validate_output or check_scope_compliance by focusing on hallucination risk, not structural or scope validation.

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 provides clear context on when to use the tool (for hallucination risk) and explains two conditional modes based on whether source_text is provided. It doesn't explicitly mention alternatives or exclusions, but the conditional behavior gives practical usage guidance.

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