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Alternatives to aurora-mcp

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    Related Servers

    • A
      license
      A
      quality
      D
      maintenance
      Deterministic time-series statistics for AI agents. This MCP server gives any LLM agent unit-tested statistical tools — anomaly detection, changepoint detection, seasonal decomposition, stationarity/trend tests, data-quality audits, baseline forecasts — with schema-validated structured output and no arbitrary code execution.
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    • A
      license
      Not graded
      quality
      B
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      Enables agents to forecast numerical trends with zero-dependency Holt linear exponential smoothing, multi-step horizons, variance confidence bands, and supporting statistical anomaly detection, regression, hypothesis testing, and PCA.
      7
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    • A
      license
      A
      quality
      A
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      30
      1
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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to detect outliers in numerical streams using multiple robust statistical strategies and to perform related analytics such as forecasting, regression, hypothesis testing, and dimensionality reduction via MCP.
      7
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    • A
      license
      Not graded
      quality
      C
      maintenance
      Provides a virtual statistician for AI agents, offering real statistical methods such as design of experiments, hypothesis testing, regression, and process control. It includes an advisor tool to recommend appropriate analyses and generates plain-language interpretations of results.
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    TDQS

    A3.9/5.0

    Scored across 7 tools

    Disambiguation4/5

    Most tools have clearly distinct roles: analyze runs the initial analysis, findings lists the results, explain provides evidence for a single claim, and load_bundle verifies shared bundles. The main ambiguity is between forecast and simulate, both forward-looking, and between analyze and findings, which both return findings; however, the descriptions give enough guidance to separate them.

    Naming Consistency4/5

    All tools share the aurora_ prefix and use lowercase snake_case, which makes them immediately recognizable. Most names are verb-led (analyze, explain, forecast, intervene, simulate), with aurora_findings being the one noun-style outlier, but the convention is still predictable.

    Tool Count5/5

    Seven tools is a well-scoped count for a statistical analysis server. Each tool addresses a distinct part of the workflow: analysis, result enumeration, evidence drill-down, bundle loading, forecasting, intervention, and simulation.

    Completeness4/5

    The core analysis lifecycle is well covered: analyze, list findings, explain evidence, and load external bundles, plus forward-looking tools for forecasting, intervention, and simulation. Minor gaps exist, such as no explicit run comparison or bundle export tool, but agents can work around these without major failures.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues