phionyx_causal_trace

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

No annotations are provided, so the description must carry full burden. It only says 'Validate a causal debugging chain' without detailing side effects, required permissions, return format (despite having an output schema), or what 'validation' entails.

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 concise, with the purpose and usage stated upfront, followed by parameter details. However, the structure could be improved by separating guidance from parameter examples.

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?

Given the simplicity of the tool (2 parameters, has output schema), the description is partially complete: it explains what and when, but omits what validation produces and any behavioral details, requiring the agent to infer from context.

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?

With schema description coverage at 0%, the description adds value by providing concrete examples for both parameters, including the arrow-separated format for causal_chain, which clarifies usage beyond the schema.

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: 'Validate a causal debugging chain' and when to call it ('when investigating a bug'). It distinguishes itself from sibling tools like phionyx_verify_claim by focusing on causal chains.

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 provides a basic usage guideline ('Call when investigating a bug'), but lacks explicit when-not-to-use or alternatives among siblings, leaving room for ambiguity.

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