A
licenseNot graded
qualityA
maintenanceEnables MCP-compatible clients to generate images and videos, synthesize speech, clone voices, and create custom voices through AI APIs.
635 npm
MIT
PicoBerry MCP Server
by UModeler
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TDQS
A4.1/5.0
Scored across 14 tools
Disambiguation4/5
Each generation tool targets a distinct input modality (text→image, text→3D, image→3D) and the post-processing tools (remesh, texture, animate, parts_board) each transform an existing asset in a clearly different way. The only real overlaps are get_asset vs wait_for_asset (polling wrapper) and get_asset vs download_asset (result URLs vs export/conversion), but the descriptions explicitly clarify the difference.
Naming Consistency3/5
Most tools follow a clean verb_noun pattern (get_credits, generate_image, list_models, wait_for_asset), but the set mixes in bare verbs (remesh, texture, animate) and a bare noun (parts_board), where `texture` also ambiguously reads as a noun. It remains readable and semantically apt, but the convention is not uniform.
Tool Count5/5
Fourteen tools map cleanly onto the platform's lifecycles: generation (3), post-processing (4), asset retrieval/inspection (3), catalog discovery (3), and billing (1). No tool feels like filler and none of the workflows require an excessive number of calls beyond the inherent async wait.
Completeness4/5
The surface covers generation, transformation, polling, export, listing, credit balance, and model/preset discovery — a full end-to-end workflow. Minor gaps exist: no asset deletion or cancellation of an in-flight task, and no way to inspect/top up credits beyond a balance read.
Maintenance
ActivityMaintained
ResponsivenessLimited data