wafle_ai_segment_compile

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

With empty annotations, the description carries the burden. It discloses the return shape ('{ dsl_json, validated, errors }') and that this is a compilation step ('Pass dsl_json straight into a new segment'), implying it does not create the segment itself. It doesn't elaborate on error types, but the key transformation behavior is clear.

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?

Two sentences pack in the purpose, example, return object, and next-step guidance. No filler or redundant language; every clause earns its place and the key information is front-loaded.

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 two-parameter tool with no output schema, the description is complete: it explains inputs (with example), output structure, and the intended follow-up action. There are no significant gaps in understanding what the tool does or how to invoke it.

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 adds value by providing a concrete natural-language example and explaining how the output (`dsl_json`) should be used, which enriches the meaning of the second parameter beyond its schema description.

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 states a specific verb ('Compile') and resource ('natural-language description') into the 'email-marketing segmentation DSL', clearly distinguishing it from sibling AI tools like translate or categorize. It also explains the output ('dsl_json') and an example input, leaving no ambiguity about the tool's function.

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 gives a clear use case with a Spanish example and instructs to 'Pass dsl_json straight into a new segment', implying the workflow context. However, it does not explicitly mention when not to use 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.