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Simba MCP Server

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by getsimba-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

    A3.5/5.0

    Scored across 94 tools

    Disambiguation2/5

    With 94 tools spanning overlapping MMM, study, recipe, revision, incrementality, campaign, and pipeline domains, many tools share nouns and require long descriptions to distinguish. Boundaries between recipe drafts, study recipes, revisions, and incrementality design/save/create operations are not self-evident to an agent without careful reading. Several descriptions include warnings about confusing related tools, which further signals latent ambiguity.

    Naming Consistency5/5

    Tool names consistently use snake_case with a predictable verb_noun pattern: list_*, get_*, create_*, update_*, run_*, set_*, and delete_*. Domain prefixes such as study_, recipe_, campaign_, pipeline_, and incrementality_ are applied consistently. Although many names are long, the convention is stable throughout.

    Tool Count1/5

    94 tools far exceeds the typical well-scoped range of 3-15 and lands in the extreme-mismatch band. Even for a broad MMM platform, this surface is so large that tool selection, documentation, and maintenance become unwieldy. The count alone indicates over-exposure rather than a tight, purpose-built tool set.

    Completeness4/5

    The surface is extensive, covering data upload/reporting, model creation and results, optimizers, scenarios, studies, recipes, quality policies, evaluations, champion tracking, campaigns, pipelines, and incrementality tests. There are some intentional gaps, such as deletion only for failed models and no API deletion for projects or unreferenced policies, but agents can generally work around these. Overall lifecycle coverage is strong, though not perfectly complete.

    Maintenance

    ActivityActive
    ResponsivenessUnresponsive

    Resources