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ModelLens

ModelLens

by Greatbeing
OverviewSchemaSourceRelated ServersDiscussionsAdmin
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

    B3/5.0

    Scored across 5 tools

    Disambiguation4/5

    Each tool maps to a distinct pipeline stage (list → probe → rank → recommend → report), so purposes are mostly clear. The only soft overlap is between 'ranking' (quality ordering from history) and 'recommend' (best model for a task profile), which could be confused by an agent wanting a single 'which is best' answer.

    Naming Consistency3/5

    Only list_models follows a verb_noun pattern; the rest mix bare verbs (probe, recommend) with bare nouns (ranking, report). The names are readable and short, but the convention is not predictable.

    Tool Count5/5

    Five tools is well-scoped for a model evaluation/benchmarking server, and each maps to a necessary stage of the workflow. No tool feels redundant or padded.

    Completeness4/5

    The surface covers listing, probing, ranking, recommending, and reporting, which is a full analysis lifecycle. However, endpoints are referenced as 'configured' yet there is no tool to add, edit, or remove endpoints, and no way to clear or inspect probe history beyond the derived ranking.

    Maintenance

    ActivityMaintained
    ResponsivenessSyncing

    Resources