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nvidia-mcp

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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4/5.0

    Scored across 10 tools

    Disambiguation5/5

    Each tool targets a distinct operation: listing models, listing categories, routing, health check, probing, single call, auto call, fanout, embedding, and similarity ranking. The few potentially overlapping pairs (list_models vs categories, route vs auto) are clearly separated by their descriptions, with route explicitly not calling and auto explicitly executing.

    Naming Consistency3/5

    Tool names are all prefixed with nv_ and are readable, but they do not follow a consistent pattern: some are verb_noun (nv_list_models), some are bare nouns (nv_categories, nv_health, nv_similarity), and some are bare verbs (nv_route, nv_probe, nv_call, nv_embed). The inconsistent use of underscores (only in nv_list_models) and the mix of verb and noun stems makes the naming scheme unpredictable.

    Tool Count5/5

    Ten tools is well-scoped for an NVIDIA model inference server: it covers catalog discovery, task routing, account health, availability probing, single and multi-model invocation, and embedding utilities. Each tool serves a distinct purpose, and the count is neither too sparse nor bloated.

    Completeness4/5

    The tool set covers the core lifecycle of working with NVIDIA models: discovering models, selecting the right model, verifying access, calling models, and embedding/similarity. Minor gaps exist, such as no dedicated method to fetch detailed model metadata (beyond list filtering) or support for multi-turn chat/streaming, but these are workarounds.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources