Audits a Blender object for production readiness: checks manifold geometry, UV maps, materials, naming, origin alignment, then returns a 0-100 score with detailed breakdown.
Full production readiness audit: manifold geometry, UV maps, materials, naming conventions, origin alignment. Returns a score 0-100 with detailed breakdown.
| Name | Required | Description | Default |
|---|---|---|---|
| object_name | Yes |
Changes observed during successful MCP inspections.
v3.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It covers what is checked and the return shape, and the word 'audit' implies a non-destructive read-only operation. However, it does not explicitly state that the tool modifies nothing, whether it operates on the named object in any special mode, or how failures or missing objects are handled.
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 tightly written sentences with no filler. The opening phrase states the tool's purpose immediately, the comma-separated list efficiently communicates the audit scope, and the return value is stated in the second sentence. Every part earns its place.
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 single-parameter, no-output-schema tool, the description provides the essential context: what is checked, what is returned, and the score range. It is slightly incomplete in not clarifying how to interpret the score or how object_name is resolved, but it is otherwise sufficient for a straightforward read-only audit tool.
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 only parameter, object_name, has 0% schema description coverage and the tool description does not elaborate on it at all. The name is self-explanatory at a basic level, but the description adds no guidance about name format, whether the object must be selected, or what happens when the object does not exist. Given the low schema coverage, the description should have compensated but did not.
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 'full production readiness audit' and enumerates the exact areas it covers (manifold geometry, UV maps, materials, naming conventions, origin alignment). It also specifies the return value (a 0-100 score with breakdown), making it easy to distinguish from narrower analysis siblings like analyze_mesh_quality or get_mesh_statistics.
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 establishes a clear use case: a comprehensive pre-production/export readiness check. It does not explicitly name when not to use it or compare it to alternatives, but the 'production readiness' framing and scope of checks give the agent enough context to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.