Generate a SkeletIQ architecture

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

Beyond the annotations, the description discloses meaningful behavior: it spends the account holder's credits, takes several minutes, uses the account holder's stored model setting rather than letting the caller choose, and returns clarification_required when the prompt names no system. It also makes clear that omitted prompt details are assumed, which is important operational context.

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 compact and front-loaded with purpose, then critical warnings about cost and latency, then behavioral context about model selection and prompt assumptions. Every sentence carries necessary information without redundancy or fluff.

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 rich input schema, output schema, and related tools, the description covers the essential operational aspects: cost, latency, non-speculative calling, routing to get_design, model selection, assumption behavior, and clarification responses. Nothing critical for correct invocation is missing; return-value details are already covered by the output schema.

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 schema already documents all parameters (100% coverage), so the baseline is 3. The description adds value beyond the schema by explaining how to write the prompt field—describe the system, its purpose, scale, and constraints—and by warning that unspecified details are assumed. This meaningfully enriches the most important parameter.

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 first sentence states a specific verb+resource: 'Design a system architecture from a prompt.' It also distinguishes itself from get_design by saying existing designs should be read rather than regenerated, which differentiates it from its closest sibling.

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 explicitly warns not to call speculatively because it spends credits and takes minutes, and names get_design as the alternative when a design already exists. It also tells the caller what to include in the prompt and how the tool responds when no system is named, giving clear conditions for use.

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