WaveGuard
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| waveguard_scanA | 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. |
| waveguard_scan_timeseriesA | Detect anomalies in time-series data using GPU-accelerated wave physics simulation. Send a flat array of numeric values and a window size. The tool automatically creates overlapping windows, uses the first N as training (normal baseline), and scores the remaining windows as test samples. Returns per-window anomaly scores, confidence, and p-values. Example: send 100 CPU-usage readings with window_size=10. The first 5 windows become training, the rest are tested. |
| waveguard_healthA | Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Use this to verify the service is running before scanning. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
The three tools have distinct purposes: health check, general anomaly scan for any data, and specialized time-series scan. However, the general scan also works on time-series data, creating ambiguity about which to use for time-series tasks.
The naming uses a common prefix 'waveguard_', but mixes verbs: 'health' is a noun, while 'scan' and 'scan_timeseries' are verbs. This inconsistency in verb style could confuse an agent expecting a uniform pattern.
With 3 tools, the server is minimal but covers the essential functions: verification, general scanning, and time-series scanning. It is slightly sparse but acceptable for a focused utility server.
The tool set covers the core workflow: health check, anomaly detection for general and time-series data. Minor gaps include lack of configuration or history retrieval, but the stateless design mitigates these.