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gpartin

WaveGuard

by gpartin
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Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v3.3.0

  • Disambiguation4/5

    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.

    Naming Consistency3/5

    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.

    Tool Count4/5

    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.

    Completeness4/5

    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.

  • Average 4.4/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 0 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations provided, so description carries full burden. It discloses statelessness, single-call usage, returns per-sample scores, confidence levels, and top features. Does not mention authorization or rate limits, but for a detection tool this is adequate.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Description is two concise paragraphs: first states purpose and key features, second provides a clear example. No unnecessary words, and front-loaded.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given 4 parameters and no output schema, the description is complete: explains workflow, return values, and includes an example. No missing critical information.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100% with well-described parameters. The description adds overall context and example but does not significantly enhance parameter meanings beyond the schema. Baseline 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Description clearly states 'Detect anomalies in data using GPU-accelerated wave physics simulation', specifying verb and resource. It distinguishes from sibling tools like waveguard_scan_timeseries by emphasizing it works on any data type and is the general-purpose version.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Explicitly describes stateless one-call operation and no separate training step, with an example. Does not explicitly state when not to use, but the mention of working on any data type implies the time-series sibling is for specialized cases.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Discloses no auth needed, checks multiple statuses. No side effects expected. With no annotations, description carries burden well.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two sentences, no fluff. First sentence states purpose, second gives usage. Front-loaded and efficient.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Complete for a no-param health check. Could optionally mention return format, but not essential for usability.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    No parameters, baseline 4. Description appropriately focuses on what the tool does.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Description clearly states it checks health, GPU, version, and engine status. Uses specific verb 'check' for resource 'WaveGuard API'. Distinguishes from sibling scan tools.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Explicitly says 'No authentication required' and recommends use before scanning. Could include when not to use (e.g., for scanning), but clear context.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries full burden. It discloses GPU acceleration, automatic overlapping windows, training on first N windows, and returns per-window anomaly scores, confidence, and p-values. It does not mention potential side effects (none expected) or resource usage, but covers core behavior well.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is two paragraphs: first explains purpose and algorithm, second gives an example. It is well-structured and efficient, though the example could be slightly more compact. No extra fluff.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    No output schema exists, so description must explain returns. It lists 'per-window anomaly scores, confidence, and p-values' – sufficient for an agent. It also covers input, algorithm, and example. Given tool complexity, this is complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema has 100% description coverage, so baseline is 3. The description adds significant value: explains window_size gives finer resolution, test_windows defaults to last ~40%, sensitivity range 0.5-5.0, and provides a concrete example linking parameters. This is exemplary.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool detects anomalies in time-series data using GPU-accelerated wave physics simulation. It specifies the input (flat numeric array, window size) and explains the algorithm. It distinguishes itself from siblings like waveguard_scan by focusing on time-series analysis.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explains when to use (detect anomalies in time-series) and provides a concrete example (CPU-usage readings with window_size=10). It does not explicitly mention when not to use or alternatives, but the context is clear enough for agents.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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