BlenderMCP

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

With no annotations provided, the description carries the full burden. It discloses key behaviors: the generated model has built-in materials, normalized size (requiring re-scaling), and returns a success/failure message. However, it lacks details on permissions, rate limits, error handling, or what 'import into Blender' entails operationally. This provides basic context but misses deeper behavioral traits.

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 appropriately sized and front-loaded, starting with the core purpose. Each sentence adds value: the first states the action, the second notes built-in materials, the third advises on scaling, and the parameter/return sections provide necessary details. There's minimal waste, though the structure could be slightly more streamlined (e.g., integrating parameter info more seamlessly).

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 tool's complexity (generating and importing 3D assets), no annotations, no output schema, and 2 parameters with 0% schema coverage, the description is moderately complete. It covers the basic operation, parameters, and return message, but lacks details on output format, error cases, integration specifics with Blender, or how it differs from siblings. This leaves gaps for an AI agent to fully understand context.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'text_prompt' is a short English description of the desired model, and 'bbox_condition' is optional, controls length/width/height ratio, and must be a list of 3 floats. This clarifies parameter purposes and constraints, compensating well for the schema gap, though it doesn't cover all potential nuances (e.g., format examples).

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 purpose: 'Generate 3D asset using Hyper3D by giving description of the desired asset, and import the asset into Blender.' It specifies the verb (generate), resource (3D asset), and technology (Hyper3D), and distinguishes it from sibling tools like 'generate_hyper3d_model_via_images' by mentioning text input. However, it doesn't explicitly differentiate from all siblings (e.g., 'import_generated_asset'), so it's not a perfect 5.

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 by stating the tool generates 3D assets via text prompts and imports into Blender, suggesting it's for text-to-3D generation. However, it doesn't explicitly state when to use this tool versus alternatives like 'generate_hyper3d_model_via_images' (which uses images) or 'import_generated_asset' (which might handle imports separately). No exclusions or clear alternatives are provided, leaving some ambiguity.

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