Server Details
Anomaly detection API powered by physics simulation. Scan any data for outliers.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
See and control every tool call
Available Tools
2 toolswaveguard_healthTry in Inspector
Check WaveGuard API health, GPU availability, version, and engine status. No authentication required.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
waveguard_scanTry in Inspector
Detect anomalies in data using GPU-accelerated wave physics simulation. Fully stateless — send training data (normal examples) and test data (samples to check) in ONE call. Returns per-sample anomaly scores, confidence levels, and the top features explaining WHY each anomaly was flagged. Works on any data type: JSON objects, numbers, text, time series, arrays. No separate training step required.
Example: to check if server metrics are anomalous, send 3-5 normal readings as training, and the suspect readings as test.
| Name | Required | Description | Default |
|---|---|---|---|
| test | Yes | 1+ data points to check for anomalies. Same type/shape as training data. Each sample is scored independently. | |
| training | Yes | 2+ examples of NORMAL/expected data. These define what 'normal' looks like. All samples should be the same type and shape. More samples = better baseline (4-10 is ideal). | |
| sensitivity | No | Anomaly threshold multiplier (default: 2.0). Lower = more sensitive. Higher = less sensitive. Range: 0.5 to 5.0. | |
| encoder_type | No | Data encoder type. Omit to auto-detect from data shape. |
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The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
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If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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