scaffold_full_application

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 does not mention whether the tool overwrites existing files, requires network access, or any side effects. The term 'generate' suggests creation, but potential destructiveness or prerequisites are unaddressed.

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?

Single sentence of 23 words, front-loaded with the main purpose. Every part adds value: verb, scope, frameworks, styling, state management. No redundancy.

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?

Despite 9 parameters and no output schema, the description is minimal. It does not explain what is returned (likely generated files) or provide usage context like directory creation or example. For a complex generation tool, more completeness is needed.

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 context like 'ML-composed pages' for mood/industry/visual_style, but does not significantly enhance understanding beyond schema descriptions.

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 generates a full project boilerplate with ML-composed pages, listing supported frameworks and styling. It distinguishes from sibling tools like scaffold_backend (backend-only) and generate_component_library (component-level) by specifying 'full project' and 'ML-composed pages'.

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 implies usage for starting a new frontend project but does not explicitly state when to use this tool versus alternatives like scaffold_backend or generate_component_library. No exclusions or when-not guidance are provided.

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