A
licenseNot graded
qualityD
maintenanceEnables to generate 3D models from text and images, apply textures, and remesh models using the Meshy AI API.
6
MIT
Meshy AI MCP Server
by pasie15
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TDQS
C2.8/5.0
Scored across 51 tools
Disambiguation3/5
Most tools are distinguishable by action prefix and modality, so create_text_to_3d_task vs create_image_to_3d_task vs create_remesh_task are reasonably clear. However, the text_to_texture task family duplicates the retexture task family using the same underlying /v1/retexture API, creating real misselection risk across five pairs of tools.
Naming Consistency5/5
The task tools consistently follow a snake_case action_resource pattern: create_, retrieve_, list_, stream_, and delete_ combined with the task type. The only deviation is get_balance, which is a unique account operation and does not disrupt overall predictability.
Tool Count2/5
At 51 tools, the surface is far larger than a practical agent-facing set, with five similar operations repeated across ten task families. Some breadth is justified by Meshy's multiple modalities, but the duplicated text_to_texture/retexture family and per-task stream/list/delete tools make it excessively heavy.
Completeness5/5
Each task family has create, retrieve, list, stream, and delete coverage, plus balance retrieval, so the lifecycle is essentially complete. No obvious domain gap prevents an agent from running generation, refinement, rigging, animation, and cleanup workflows.
Maintenance
ActivityStale
ResponsivenessNo issues