Skip to main content
Glama

WaveGuard

by com.emergentphysicslab

waveguard_instability

Read-onlyIdempotent

Estimate instability under controlled perturb-and-resolve trials.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
testYes1+ candidate samples to stress-test with perturbation trials.
trialsNoNumber of perturbation trials per sample.
trainingYes2+ baseline normal samples for reference dynamics.
field_levelNo0 = real scalar field, 1 = complex field.
sensitivityNoAnomaly sensitivity multiplier (default: 1.0).
encoder_typeNoOptional encoder override. Omit to auto-detect.
perturbation_strengthNoRelative perturbation amplitude applied during instability assay.

Schema Changelog

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

  1. Changed7 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: +"0 = real scalar field, 1 = complex field."
    • addedInput schema / properties / perturbation_strength / description
      Added value: +"Relative perturbation amplitude applied during instability assay."
    • addedInput schema / properties / sensitivity / description
      Added value: +"Anomaly sensitivity multiplier (default: 1.0)."
    • addedInput schema / properties / test / description
      Added value: +"1+ candidate samples to stress-test with perturbation trials."
    • addedInput schema / properties / training / description
      Added value: +"2+ baseline normal samples for reference dynamics."
    • addedInput schema / properties / trials / description
      Added value: +"Number of perturbation trials per sample."
  2. Added

TDQS

C2.7/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description does not contradict them. However, the description adds little behavioral context beyond the method name; it does not explain what happens during trials, what outputs are expected, or any limitations not already covered by annotations.

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

Conciseness2/5

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

The description is a single short sentence, which is front-loaded but under-specified. While it is not verbose, the brevity sacrifices useful detail that should be present, especially given the tool has 7 parameters and no output schema.

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?

With 7 parameters, a rich set of annotations, and no output schema, the description is too terse to be considered complete. It omits context about required inputs, the nature of 'instability', expected return format, and how it relates to sibling tools, leaving the agent with insufficient information for correct invocation.

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 coverage is 100%, with each parameter already having a description in the schema. The tool description itself provides no parameter-specific semantics, so it adds no extra value beyond the schema, which corresponds to the baseline 3.

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 a specific verb ('Estimate') and resource ('instability') and adds the method ('controlled perturb-and-resolve trials'), clearly indicating what the tool does. However, it does not differentiate this from siblings like waveguard_cascade_risk or waveguard_mechanism_probe, so it misses the highest bar for distinction.

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?

There is no guidance on when to use this tool versus alternatives. The description implies it is for instability estimation, but it never states scenarios, exclusions, or comparisons such as 'use this instead of X when Y' which would help an agent select it correctly.

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

All WaveGuard toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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