conversational_blender_assistant

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

Annotations already declare read-only and idempotent behavior; the description adds valuable behavioral context such as probing capabilities before responding and graceful fallback when sampling is unavailable. No contradiction with annotations is present.

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 two sentences, front-loaded with the tool's role, and contains no filler or redundant repetition of schema/annotations. The SEP-1577 reference is technical but concise.

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?

With annotations and an output schema present, the description sufficiently conveys the core behavior of answering Blender questions via multi-step sampling and fallback. It does not mention sibling alternatives for selection clarity, but the core usage context is adequately covered.

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 input schema provides full descriptions for all three parameters (100% coverage), including defaults and formatting hints for context_level. The description adds no extra parameter-specific meaning, but the baseline 3 is appropriate since the schema already carries the burden.

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 identifies the tool as a conversational Blender assistant for answering natural-language Blender questions, mentioning SEP-1577 multi-step sampling and capability probing. This distinguishes it from some siblings like blender_help, though the core verb 'answer' is implied rather than explicit.

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 use for conversational Blender questions requiring multi-step reasoning, but it does not explicitly state when to use this tool versus alternatives like ai_chat or blender_help, nor does it provide exclusions or when-not-to-use guidance.

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