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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

    A3.6/5.0

    Scored across 63 tools

    Disambiguation3/5

    The core model/upload/optimizer/scenario tools are distinct, but the recipe and study clusters overlap heavily: create_recipe_draft vs create_study_recipe and get_recipe_revision_authoring vs get_recipe_revision are easy to confuse from names alone. Detailed descriptions resolve most ambiguity, but an agent must read carefully before choosing.

    Naming Consistency5/5

    All 63 tools follow a consistent snake_case verb_noun pattern: get_*, list_*, create_*, update_*, run_*, launch_*, evaluate_*, compare_*. Even the longer compound nouns like study_validation_pair and recipe_revision_authoring are internally consistent with their siblings, so the set is highly predictable.

    Tool Count2/5

    At 63 tools, the server is far above the well-scoped range and feels like a full platform API rather than a focused MCP surface. The broad Simba domain explains some of the count, but the surface would be much more usable split into modeling, run/optimization, and study-governance servers.

    Completeness4/5

    The surface covers nearly the entire MMM and VAR lifecycle: schema/upload, model creation/status/results, optimizer/scenario runs, projects, and deep study/recipe/quality governance. It is not a perfect 5 because a few lifecycle actions such as deleting non-failed models/projects, champion promotion, and manual sign-off are deliberately frontend-only and documented as gaps an agent cannot complete.

    Maintenance

    ActivityActive
    ResponsivenessUnresponsive

    Resources