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MLflow MCP Server

by kkruglik
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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

    A3.6/5.0

    Scored across 40 tools

    Disambiguation5/5

    Each tool targets a distinct MLflow resource or action, such as experiments, runs, models, artifacts, or tags. Despite a large number of tools, their descriptions clearly differentiate them (e.g., get_run vs get_runs vs query_runs). There is minimal overlap in functionality.

    Naming Consistency5/5

    Tool names follow a consistent verb_noun pattern (e.g., get_experiment, set_model_alias, delete_run). Exceptions like 'health' are single-purpose and follow the pattern. Overall naming is predictable and clear.

    Tool Count3/5

    40 tools is above the typical well-scoped range (3-15), but the domain (MLflow) has many entities and operations, making a larger surface reasonable. However, some tools could be merged or omitted to reduce cognitive load.

    Completeness4/5

    The tool surface covers CRUD for experiments, runs, model registry, tags, and artifacts, plus search and comparisons. Minor gaps exist, such as no direct create_run or update_experiment, but these are managed elsewhere in MLflow.

    Maintenance

    ActivitySlowing
    ResponsivenessUnresponsive

    Resources