Server has 4 tools with minimal schema and description quality. Three tools have empty input schemas ({}), making them impossible for LLMs to understand parameter requirements. The one tool with a parameter (mobu_scene__save_scene) has a schema but lacks descriptions for what 'path' means in context. Tool descriptions are present but generic (10-50 chars), below the 50-200 char baseline for LLM-optimized descriptions. No output schemas documented. No error handling guidance. No tool annotations (readOnlyHint/destructiveHint). Naming is acceptable (verb_noun pattern) but descriptions fail to explain WHEN to use each tool or what it returns.
Return host and embedded-Python version evidence.
Return concise scene metadata.
Return model names without exposing a raw scripting surface.
Save the scene as an FBX file at an explicit absolute path.
Three tools have empty input schemas ({}). LLMs cannot infer parameter requirements from empty schemas.
All tool descriptions are under 50 characters and lack WHEN/WHY context. 'Return host and embedded-Python version evidence' (47 chars) does not explain when to call ping vs other diagnostics, or what the LLM should do with the result.
No output schemas documented. LLMs cannot plan downstream calls or extract required fields (e.g., does inspect_scene return model_count, scene_name, or both?). Baseline: 100% of A+ tools document return types.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 38 | 2026-07-28+ | v2 |
No tool annotations (readOnlyHint, destructiveHint, idempotentHint). save_scene is marked WRITE but lacks destructiveHint annotation. Agents cannot distinguish safe from risky operations.
No error handling guidance. If save_scene fails (invalid path, permission denied, scene locked), the tool provides no recovery hint.