akai_sli

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

No annotations are provided, so the description must disclose behavioral traits. It only mentions accepted file types and that it performs inference, but does not describe side effects, safety (read-only vs destructive), authorization needs, or any other runtime behavior.

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 extremely concise (one sentence) but lacks essential details, making it under-specified rather than efficiently informative. It does not justify its brevity by covering key aspects.

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 complexity of an inference tool, the description is insufficient. No output schema or return value information is provided, and behavioral expectations are missing, leaving the agent underinformed.

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 has 100% description coverage for both parameters (args and stdin). The tool description does not add any information beyond what the schema already provides, defaulting to baseline 3.

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 'Structured Layer Inference' and acceptable file types, providing a general purpose. However, the term is abstract and does not differentiate from many sibling tools like akai_gen, akai_reason, or akai_run, leaving ambiguity about what specific inference action is performed.

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?

No guidance on when to use this tool versus alternatives. The description lacks context about prerequisites, input constraints, or suitable scenarios for invocation.

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