scaffold_kotlin_ai_module

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

No annotations are provided, so the description carries the full burden. It does disclose the content of the generated module (Flow streaming, ViewModel state integration) which is useful behavioral context. However, it does not mention side effects such as file creation, overwriting, or project structure requirements, which would be important for a scaffolding operation.

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?

A single, information-dense sentence with no wasted words. The core action and resource are front-loaded, and the additional detail about Flow streaming and ViewModel integration earns its place by clarifying the generated module's architecture.

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 purpose and key output characteristics, but with no output schema or annotations, important contextual information is missing: what files/artifacts are generated, whether the tool modifies existing code, and any prerequisites about the target Kotlin project. It is adequate for a simple scaffolding tool but not fully complete.

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 description coverage is 67%, with featureName and systemInstruction already described in the schema. The description adds some context by mentioning the frameworks that map to the provider enum, but it does not explicitly explain how provider or systemInstruction influence the scaffolded output. It partially compensates for the undocumented provider parameter but not fully.

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 uses a specific verb ('Scaffolds') and names the resource precisely: 'Google GenAI Kotlin SDK / MediaPipe on-device LLM inference modules'. It adds concrete technical details (Flow streaming, ViewModel state integration) that clearly distinguish it from sibling scaffold tools such as scaffold_kotlin_backend or scaffold_kotlin_database.

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 clearly identifies the intended usage context: scaffolding AI inference modules for Kotlin using specific on-device/GenAI frameworks. It implies when to use this tool over siblings by naming the niche, but it does not explicitly state exclusions or point to alternative tools.

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