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easydeploy-ai-mcp

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    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 28 tools

    Disambiguation4/5

    Most tools target clearly distinct resources and actions, and the upload/train/predict flows are well explained. Minor overlap exists between get_training_status and model-version status tracking, and between complete_upload and create_dataset_version for registering dataset versions.

    Naming Consistency5/5

    Tool names consistently use snake_case with predictable verb_noun phrasing such as list_projects, get_dataset, create_model_version, and run_batch_prediction. The few create_* tools that also update still follow the same naming convention.

    Tool Count3/5

    28 tools is heavy, though it reflects a broad MLOps platform spanning projects, datasets, versions, models, training, and predictions. Several list/get pairs could likely be consolidated, but few tools are clearly redundant.

    Completeness4/5

    The core ML lifecycle is well covered: upload, dataset/model/version creation, training, reporting, and ad-hoc/batch prediction. Gaps remain around deletion operations, model-version updates, and endpoint/deployment management hinted at by account status.

    Maintenance

    ActivityNo data
    ResponsivenessNo issues

    Resources