A
licenseA
qualityB
maintenanceEnables AI agents to search, compare, and right-size Hugging Face models, including estimating whether a model fits in available GPU VRAM before downloading it.
5
21 PyPI
MIT
vram-mcp
by sushiHex
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TDQS
A3.5/5.0
Scored across 13 tools
Disambiguation5/5
Each tool targets a distinct operation: claim/renew/release manage claims, reserve handles capacity reservations, unload/ensure_free/warm manage model residency, while vram_status/list_loaded/list_claims/history/trend provide distinct read views. Even similar actions like unload vs ensure_free are clearly separated by scope (single model vs threshold-based eviction).
Naming Consistency3/5
Naming uses a mix of bare verbs (renew, release, advise, unload, warm, claim, reserve), verb_noun snake_case (list_claims, list_loaded, ensure_free), and noun-only identifiers (vram_status, history, trend). The pattern is somewhat predictable by category (actions vs queries), but it lacks a single consistent convention, making it less uniform than an all-verb_noun set.
Tool Count5/5
13 tools is well within the ideal 3–15 range and each tool addresses a meaningful aspect of VRAM management: claims, reservations, eviction, warming, status, history, and trend. No tool feels redundant or superfluous.
Completeness4/5
The surface covers the core lifecycle well: claim/renew/release, reserve, warm/unload/ensure_free, plus status, history, and trend. A minor gap is the lack of a dedicated list_reservations tool—list_claims seems focused on model claims, so capacity reservations may be invisible unless they appear in status/history.
Maintenance
ActivityMaintained
ResponsivenessWithin a week