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SageMaker AI MCP Server

by dgallitelli
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Python
Remote

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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

    C2.9/5.0

    Scored across 57 tools

    Disambiguation5/5

    Each tool targets a specific resource-action pair, and even closely related resources like pipelines and pipeline executions are clearly distinguished by their names and descriptions. There is no meaningful overlap that would cause selection ambiguity.

    Naming Consistency5/5

    All tools follow a consistent <verb>_<resource>_sagemaker pattern, using snake_case throughout. Verb choice (list, describe, create, delete, start, stop) is uniform, and the resource portion is descriptive and predictable.

    Tool Count1/5

    With 57 tools, the server is far beyond the typical well-scoped range and exceeds even the 'too many' threshold. The sheer number is overwhelming and suggests an extreme mismatch for an MCP server, even considering the breadth of SageMaker.

    Completeness2/5

    The tool set covers many resources but lacks fundamental lifecycle operations such as create or update for core entities like training jobs, models, endpoints, and pipelines. Several listed resources have no describe or delete counterparts, leaving significant gaps that hinder agent workflows.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources