Echo
Related Servers
Alternatives to Echo
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceEnables zero-dependency statistical analysis of numerical and time-series streams, flagging outliers via standard Z-score, modified median absolute deviation (MAD), and Tukey IQR fences. Also supports trend forecasting with Holt linear smoothing, multivariate gradient-descent regression, Welch's t-test hypothesis testing, and Power Iteration PCA dimensionality reduction through a native MCP stdio interface.7MIT
- AlicenseAqualityDmaintenanceDeterministic 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.17MIT
- AlicenseNot gradedqualityCmaintenanceEnables incident detection and analysis by identifying anomalies in metric time series and surfacing root-cause candidates and recommended actions. Supports both mock (synthetic) and VictoriaMetrics backends with identical MCP tool contracts for seamless development-to-production switching.MIT
- AlicenseNot gradedqualityBmaintenanceEnables 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.7MIT
- AlicenseAqualityDmaintenanceTimeseries anomaly analysis for fleet telemetry, delivered as an MCP server.1Academic Free v1.1
- AlicenseAqualityDmaintenanceAn MCP server that exposes GPU-accelerated anomaly detection to AI assistants via the Model Context Protocol. Provides two MCP tools: waveguard_scan (send training + test data in one call, returns per-sample anomaly scores and top explanatory features) and waveguard_health (check API and GPU status). Works on time series, JSON, numbers, text, and images — fully stateless.34MIT
TDQS
Scored across 2 tools
The two tools are distinguishable by input source (in-memory list vs. CSV file path), so an agent can pick correctly. However, analyze_csv is explicitly documented as 'same as analyze_series' but with a file loader, meaning they are functionally the same operation with overlapping purpose and only a thin input-format boundary.
Both names follow a clean, predictable verb_noun snake_case pattern (analyze_series, analyze_csv) with a shared 'analyze_' prefix that signals the common family. No deviation or mixed convention.
With only two tools, the surface is thin — it amounts to one core algorithm exposed through two input adapters. That is borderline minimal rather than a well-rounded set, though the narrow scope keeps it from being egregious.
For time-series anomaly/pattern discovery the pair does cover list and CSV inputs, but there are notable gaps: no output for the raw Matrix Profile, no way to write/export results, and no multi-column or batch analysis. Agents can work around these but the lifecycle feels partial.