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Alternatives to tslab-mcp

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    Related Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      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
      Not graded
      quality
      C
      maintenance
      Enables time-series analysis and forecasting through a structured tool catalogue, including data loading, quality repair, diagnostics, and forecasting with ARIMA, exponential smoothing, Chronos-2, Toto 2.0, and AutoML.
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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to run TimesFM-3 forecasting workflows locally, including joint multivariate forecasting with known future drivers, backtesting against a baseline, what-if scenario comparison, and historical anomaly detection. It exposes the studio's tools and bundled public and synthetic demo datasets over MCP.
      Apache 2.0
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables statistical forecasting and anomaly detection over numerical time-series data using Holt linear smoothing, multi-step projections, and variance-based confidence bands.
      7
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    • 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.
      17
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    TDQS

    A4.6/5.0

    Scored across 8 tools

    Disambiguation5/5

    Each tool has a distinct and well-defined purpose within the time series forecasting workflow: loading, describing, listing models, cross-validating, forecasting, detecting anomalies, and exporting results. There is no overlap or ambiguity between tools.

    Naming Consistency5/5

    All tools follow a consistent pattern: the prefix 'tsf_' followed by a verb (and optional noun), all in snake_case. Examples include tsf_load_series, tsf_describe_series, tsf_cross_validate, and tsf_export_report. The naming is predictable and uniform.

    Tool Count5/5

    With 8 tools, the server covers a complete analysis pipeline without excess. Each tool is necessary and corresponds to a clear step in the workflow, from data loading to report generation. The count is well-scoped for the domain.

    Completeness5/5

    The tool surface covers the full lifecycle of a typical time series analysis: load data, explore features, check available models, cross-validate, forecast, detect anomalies, and export manifests/reports. There are no obvious gaps; the workflow feels self-contained and actionable.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues