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vastai-mcp

by dam2452
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Python

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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

    A4.1/5.0

    Scored across 20 tools

    Disambiguation5/5

    Each tool targets a distinct resource and action: search_offers/search_benchmarks/search_templates operate on different datasets, get_gpu_metrics vs get_gpu_trends differ by snapshot vs history, and instance tools are clearly separated (list/get/create/manage/destroy). Even potentially similar tools like execute_command and ssh_exec are explicitly differentiated by stopped vs running instance state.

    Naming Consistency5/5

    All tools follow a consistent verb_noun snake_case pattern (search_offers, list_instances, create_instance, destroy_instance, ssh_exec, ssh_transfer). The naming is predictable and consistent, with no camelCase or mixed conventions.

    Tool Count4/5

    20 tools is at the upper end of 'well-scoped' but appropriate for a full Vast.ai API surface covering marketplace search, instance CRUD, SSH operations, metrics, and user info. The count feels justified given the breadth of features, though slightly heavy compared to a typical 3-15 tool server.

    Completeness4/5

    The surface covers the main GPU rental workflow: search offers, create/read/delete instances, manage state, run commands, transfer files, and fetch metrics. Minor gaps like volume management exist, but the raw_request escape hatch covers any missing endpoint, preventing dead ends.

    Maintenance

    ActivityStale
    ResponsivenessNo issues

    Resources