Process Multilingual Input

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

The description adds behavioral context beyond annotations, detailing the pipeline stages (detect, normalize, extract, safety check) and safety blocking behavior, with no contradiction to annotations. Annotations are already informative, but the description enhances understanding.

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 well-structured with clear sections (pipeline, languages, args, returns, examples) and front-loads the purpose. It is slightly verbose but efficient overall.

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 (pipeline, safety, code-switching) and absence of an output schema, the description provides return format and examples, making it fairly complete. Minor gaps remain (e.g., safety check details beyond example), but overall sufficient.

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%, setting a baseline of 3. The description provides additional context for each parameter (e.g., text as raw input, execute default true) and includes helpful examples, exceeding the baseline.

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 it processes mixed-language input through a defined pipeline, lists supported languages, and distinguishes it from siblings like neuroverse_route or neuroverse_reason by specifying its role as the central processing tool.

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?

While the description implies usage for multilingual input processing, it lacks explicit guidance on when not to use it or how it compares to alternatives like neuroverse_route or neuroverse_execute.

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