generate_blender_script

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

Annotations are all false, so the description carries the burden of disclosure. It does mention the return format (success, script, error) and the generation nature (not execution), but fails to disclose prerequisites like Ollama running or potential failure modes. Moderate transparency, but not rich enough to fully anticipate behavior.

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 concise and well-structured with a clear purpose line, a return format section, and a practical example. Every section adds value, and the formatting aids quick understanding. No wasted words.

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?

For a simple generation tool, the description covers purpose, return format, and an example. However, it lacks error conditions, prerequisites (e.g., Ollama availability), and differentiation from similar siblings. Given the 3 parameters and output schema, additional context on when to use this tool and what could go wrong would improve completeness.

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%, and the description does not systematically explain the parameters. The example shows 'prompt' and 'model' in use, but does not define ollama_url or clarify the meaning of 'model' beyond being a parameter. This is insufficient for a 3-parameter tool where the description must compensate.

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 generates a Blender Python script from a natural language prompt using Ollama, with a specific verb and resource. It adds specificity by mentioning 'Blender Python script' and 'local LLM (Ollama)', but does not explicitly distinguish it from similar siblings like blender_ai_generate.

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?

No explicit guidance on when to use this tool versus alternatives. The description implies use when a natural language prompt needs to be converted to a Blender script, but sibling tools that might overlap (e.g., blender_ai_generate) are not mentioned, offering no exclusion criteria or decision framework.

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