d3p_check-hallucination

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

With no annotations, the description carries full burden. It mentions 'Returns confidence score and warnings' and 'Cost: 10 sats,' which adds some behavioral context. However, it does not disclose rate limits, authentication needs, error handling, or any destructive impact. It is not misleading but incomplete.

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 moderately concise, mixing a plain sentence with a parameter block. It is functional but could be more structured (e.g., separating usage notes from technical details). The parameter info is front-loaded but followed by repeated 'Category:' and 'Cost:' lines that add clutter.

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 description covers the core function and parameter structure, and an output schema exists (from context signals), so return values need not be explained. However, it lacks details on input constraints (e.g., text length), error conditions, and whether the tool modifies state. These gaps reduce completeness for paid tool usage.

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 input schema only defines a single 'params' string property with default '{}', providing no semantics. The description compensates by explaining that 'params' should contain a 'response' string field for the AI-generated text. This adds essential meaning beyond the schema, making the tool usable.

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's purpose: 'Analyze AI-generated text for hallucination risk based on linguistic markers.' It specifies the verb 'analyze' and resource 'AI-generated text for hallucination risk,' distinguishing it from sibling tools like d3p_vibe-check or d3p_emoji-sentiment, which focus on different analysis types.

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 no explicit when-to-use or when-not-to-use guidance. It only lists 'Category: analysis' and cost, implying it's suitable for hallucination detection but lacking exclusion criteria or alternatives. No sibling differentiation or context for when to avoid this tool.

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