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easydeploy-ai-mcp

easydeploy-ai-mcp

Official
by EasyDeploy-AI
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Python
Hybrid

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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

    A3.8/5.0

    Scored across 30 tools

    Disambiguation4/5

    Most tools map cleanly to distinct resources and actions, with clear separation between uploads, datasets, model versions, training, aliases, and predictions. A few boundaries could still be confused, especially get_model vs get_model_version, list_dataset_versions vs get_dataset_version, and complete_upload vs create_dataset_version, but the descriptions generally resolve them.

    Naming Consistency5/5

    The tool set uses a consistent snake_case verb_noun convention throughout: list_*, get_*, create_*, update_*, run_*, submit_*, set_*, start_*, and complete_*. upload_from_url is the only slightly different verb phrase, but it remains clear and consistent with the overall style.

    Tool Count3/5

    Thirty tools is heavy for any single MCP server, even one covering a full ML lifecycle. Most tools are purposeful and non-redundant, but the surface could likely be consolidated through fewer generic resource tools or combined list/get operations.

    Completeness4/5

    The surface covers the core ML workflow well: projects, uploads, datasets and versions, models and versions, aliases, training, reports, predictions, account status, and a playbook. Minor lifecycle gaps remain, such as deletion/cleanup operations for projects, datasets, models, versions, or aliases.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

    Resources