Check WaveGuard API health, GPU availability, version, and engine status. No authentication required. Returns status, version, and GPU info.
| Name | Required | Description | Default |
|---|---|---|---|
| verbose | No | Return detailed health info including memory and uptime (default: false). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, establishing safety. The description adds that no authentication is required and details the return content (status, version, GPU info), which is useful context beyond typical schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff. The first sentence front-loads the core purpose, and the second adds authentication and return-value details. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity health check tool with one optional parameter and no output schema, the description covers purpose, auth, and return content. It doesn't explain failure behavior, but that's minor for a health endpoint. Overall, the description is sufficient for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'verbose' is fully described in the schema (100% coverage), so the description doesn't need to repeat it. The description's mention of return values ('Returns status, version, and GPU info') hint at what verbose might control, but doesn't explicitly clarify the parameter's effect. Baseline 3 is warranted.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Check' with a clear resource 'WaveGuard API health', enumerating GPU availability, version, and engine status. This clearly distinguishes it from sibling tools that perform various analyses. The mention of return values reinforces the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies 'No authentication required', giving a clear precondition for use. It implies this is a preliminary health check tool, though it doesn't explicitly contrast with siblings. The context is clear enough for an agent to choose this when needing system status before other operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools occupy overlapping anomaly-detection territory (scan, scan_timeseries, price_manipulation, volume_check, token_risk, wallet_profile), which could cause misselection when an agent needs generic vs. specialized analysis. However, descriptions clarify data types and use cases, so the overlap is manageable.
All tools share the consistent 'waveguard_' prefix with descriptive underscore-separated names (e.g., waveguard_cascade_risk, waveguard_volume_check). The occasional verb like 'scan' or 'compare' fits the overall pattern, making the set highly predictable.
With 19 tools, the server is on the heavy side for a typical MCP but not extreme. The breadth reflects a comprehensive risk-analysis platform, though some specialized detectors (e.g., waveguard_price_manipulation vs. waveguard_scan_timeseries) could potentially be consolidated without losing functionality.
The tool surface covers the full analytical workflow: data ingestion (market_data), generic anomaly detection (scan, scan_timeseries), specialized crypto risk (token_risk, volume_check, wallet_profile), structural similarity (fingerprint, compare), and scenario/impact analysis (counterfactual, cascade_risk, mechanism_probe). Minor gaps like direct report generation exist but are not critical for the core purpose.