Waveguardclient MCP Server

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基本信息

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其他

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stdio

作者

gpartin

提交者

gpartin

关于 Waveguardclient MCP Server

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Waveguardclient MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "WaveGuardClient": {
      "command": "python",
      "args": [
        "benchmarks/benchmark_vs_sklearn.py"
      ]
    }
  }
}

Waveguardclient MCP Server 工具

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工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Waveguardclient?

Waveguardclient is a Python SDK for the WaveGuard API, a general-purpose anomaly detection service that uses GPU-accelerated wave physics instead of machine learning. It accepts any structured, numeric, or text data—server metrics, financial transactions, log files, sensor readings, time series—and returns anomaly scores, confidence levels, and explanations of which features triggered the alert. Designed for developers and AI agents who need zero-config anomaly detection without ML expertise.

How to use Waveguardclient?

Install via pip install WaveGuardClient, create a WaveGuard instance with your API key, then call scan() on your training and test data. For MCP integration with Claude Desktop, add a waveguard entry to your mcpServers config using uvx --from WaveGuardClient waveguard-mcp. All data types are auto-detected—no configuration needed.

Key features of Waveguardclient

Stateless API: one call per detection, no training pipelines
Works on JSON objects, arrays, text, and time series
GPU-accelerated wave physics (no ML models, no drift)
Returns anomaly scores, confidence, and per-feature explanations
MCP server for Claude Desktop with three built-in tools
Requires only 2+ training samples to define "normal"

Use cases of Waveguardclient

DevOps server monitoring (CPU, memory, latency) to detect memory leaks or DDoS
Financial fraud detection on transactions (amount, velocity, location)
Security log analysis catching SQL injection, crypto miners, or privilege escalation
IoT predictive maintenance from sensor readings (temperature, pressure, vibration)
Time-series monitoring of metric windows for spikes or flatlines

常见问题

What are the dependencies to use Waveguardclient?

Only Python and the `requests` library. All physics computation runs server-side on GPU—you do not need a GPU locally.

How does Waveguardclient compare to traditional ML anomaly detectors?

It uses wave physics instead of machine learning, so there is no training step, no model drift, no hyperparameter tuning. Benchmarks show it achieves an average F1 of 0.76, competitive with scikit-learn’s IsolationForest (0.74), while requiring zero ML expertise.

Where does my data go when I send it to the API?

Data is encoded onto a lattice, processed on a GPU, and then torn down after each call. Nothing is stored on the server—the system is fully stateless.

What data types does the API support?

JSON objects, numeric arrays, text strings, and time-series windows are all auto-detected from the data shape. No manual type specification is required except for text (set `encoder_type="text"`).

How do I authenticate with the API?

You provide an API key when creating the `WaveGuard` instance: `WaveGuard(api_key="YOUR_KEY")`. The key is passed in the request header.

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