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Glama

WaveGuard

by com.emergentphysicslab

waveguard_phase_coherence

Read-onlyIdempotent

Measure coherence/entropy and collapse-risk indicators for candidate data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
testYes1+ candidate samples to evaluate for phase coherence and entropy.
trainingYes2+ baseline normal samples for reference coherence metrics.
field_levelNoField representation level. Default 1 for phase-aware analysis.
sensitivityNoAnomaly sensitivity multiplier (default: 1.0).
encoder_typeNoOptional encoder override. Omit to auto-detect.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changed
    • addedInput schema / properties / encoder_type / description
      Added value: +"Optional encoder override. Omit to auto-detect."
    • addedInput schema / properties / field_level / description
      Added value: +"Field representation level. Default 1 for phase-aware analysis."
    • addedInput schema / properties / sensitivity / description
      Added value: +"Anomaly sensitivity multiplier (default: 1.0)."
    • addedInput schema / properties / test / description
      Added value: +"1+ candidate samples to evaluate for phase coherence and entropy."
    • addedInput schema / properties / training / description
      Added value: +"2+ baseline normal samples for reference coherence metrics."
  2. Added

TDQS

C2.9/5.0
Behavior2/5

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

The annotations already declare the tool as read-only, idempotent, and non-destructive. The description does not add any behavioral traits beyond this, such as return format, processing behavior, or limitations. It only restates the analytical focus, which falls short of disclosing additional behavioral context.

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?

The description is a single, front-loaded sentence that directly states the tool's purpose without filler. It is concise and every word contributes to the message, though it may be too terse for complex functionality.

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

Completeness2/5

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

Given the tool's complexity (5 parameters, 2 required, no output schema) and the presence of many sibling tools, this one-sentence description is insufficient. It does not explain what 'phase coherence' means, what the output looks like, or how it relates to other waveguard tools, leaving significant gaps for an AI agent.

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?

The input schema already provides detailed descriptions for all five parameters, covering 100% of them. The description adds no additional meaning about how parameters interact or influence results, so the baseline score of 3 is appropriate.

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

Purpose4/5

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

The description uses the verb 'Measure' with specific objects 'coherence/entropy' and 'collapse-risk indicators' for 'candidate data', clearly indicating the tool's analytic function. However, it does not explicitly differentiate from sibling tools like waveguard_cascade_risk, which also addresses collapse risk, so it's clear but lacks explicit sibling differentiation.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like waveguard_instability or waveguard_cascade_risk. There are no prerequisites, exclusions, or context clues about typical use cases beyond the general 'candidate data' phrase.

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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TDQS

B3.4/5.0
Disambiguation3/5

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.

Naming Consistency5/5

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.

Tool Count3/5

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.

Completeness4/5

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.

Resources