render_quad_views

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 discloses significant behavioral traits: the fork-render-policy redirect under specific hardware/software conditions, the consent_token requirement for karma engines, and that it returns a dict rather than a string. This goes beyond basic schemas and adds concrete environmental 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 moderately concise, front-loading a critical note before stating the core purpose. It contains a mixed-language structure and a slightly verbose redirect explanation, but each sentence contributes useful information without excessive padding.

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 purpose, return type, and edge-case behavior, but lacks an output schema and detailed parameter narratives. It references render_single_view for Pydantic background, which offloads some explanation. For a tool with four parameters and no output schema, it is adequate but not fully comprehensive.

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 0%, so the description must compensate. It partially does by mentioning karma_engine values (karma_cpu/karma_xpu) and render_engine (opengl), but it doesn't explain render_path or fully elaborate on consent_token semantics. Only about half the parameters receive meaningful contextual explanation.

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 the tool's function: 'Render 4 canonical views from Houdini and return a structured result dict.' It specifies the resource (Houdini) and scope (4 canonical views), distinguishing it from render_single_view by count. The return type is also clarified, resolving ambiguity.

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?

Provides conditional usage guidance: in environments without OGL 3.3, the opengl renderer is redirected to capture_pane_screenshot, and karma engines require a consent_token. It also mentions the legacy command name for backward compatibility. While it doesn't explicitly say when to prefer this over alternatives, the context is clear.

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