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      license
      A
      quality
      B
      maintenance
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    • F
      license
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      quality
      C
      maintenance
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    • A
      license
      A
      quality
      B
      maintenance
      Enables analysts and agents to specify macroeconomic scenarios and portfolio positions, then run Bayesian VAR stress tests and retrieve loss percentiles, expected shortfall, and scenario severity through MCP tools.
      10
      144 PyPI
      5
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
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      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
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    • A
      license
      Not graded
      quality
      D
      maintenance
      MCP server that drives EViews econometrics software from Python or LLM clients via COM automation, enabling workfile creation, model estimation, diagnostics, and data exchange with pandas.
      1
      MIT

    TDQS

    B3.2/5.0

    Scored across 44 tools

    Disambiguation3/5

    The staged workflow gives most tools distinct roles, but several near-parallel tools are easy to confuse: load_data/load_pre/split_inp for entry, estimate/estimate_vec for model class, identify_cross/matrices/echelon for identification, and evaluate/forecast_uncertainty for comparison. The long, high-context descriptions help, but selection still depends on knowing the analyst's current route and model state.

    Naming Consistency4/5

    Names are consistently snake_case with recognizable prefixes such as load_, plot_, identify_, and job_. The grammar varies between verb-only, noun-only, and verb_noun forms, but the convention remains readable and predictable overall.

    Tool Count2/5

    Forty-four tools is very heavy for a single MCP server, even one modeling a full econometric workflow. Many tools map to legitimate stages, but the surface is large enough to overwhelm selection and orchestration without a higher-level workflow wrapper.

    Completeness4/5

    The surface covers the major lifecycle: raw/univariate entry, gating, identification, estimation, VEC analysis, forecasting, diagnostics, plotting, jobs, decisions, and export. Minor gaps remain around explicit session/model-state inspection and standalone handling of some implicitly parameterized actions such as set/adopt.