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Glama

WaveGuard

by com.emergentphysicslab

waveguard_cascade_risk

Read-onlyIdempotent

Estimate shock propagation and resilience from adjacency-linked entities.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entitiesYes2+ entities/nodes participating in the cascade graph.
field_levelNoField representation level. Default 1 for graph interaction dynamics.
sensitivityNoAnomaly sensitivity multiplier (default: 1.0).
encoder_typeNoOptional encoder override. Omit to auto-detect.
shock_indicesYesIndices of initially shocked entities within the entities array.
shock_strengthNoInitial perturbation magnitude injected at shock indices.
adjacency_matrixYesN×N weighted adjacency matrix describing link strengths between entities.
training_contextYes2+ baseline context samples used for normalization.

Schema Changelog

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

  1. Changed8 schema fields changed
    • addedInput schema / properties / adjacency_matrix / description
      Added value: +"N×N weighted adjacency matrix describing link strengths between entities."
    • addedInput schema / properties / encoder_type / description
      Added value: +"Optional encoder override. Omit to auto-detect."
    • addedInput schema / properties / entities / description
      Added value: +"2+ entities/nodes participating in the cascade graph."
    • addedInput schema / properties / field_level / description
      Added value: +"Field representation level. Default 1 for graph interaction dynamics."
    • addedInput schema / properties / sensitivity / description
      Added value: +"Anomaly sensitivity multiplier (default: 1.0)."
    • addedInput schema / properties / shock_indices / description
      Added value: +"Indices of initially shocked entities within the entities array."
    • addedInput schema / properties / shock_strength / description
      Added value: +"Initial perturbation magnitude injected at shock indices."
    • addedInput schema / properties / training_context / description
      Added value: +"2+ baseline context samples used for normalization."
  2. Added

TDQS

B3.2/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds no behavioral context beyond what annotations provide—no mention of output format, computational complexity, or assumptions. It merely restates a read-only estimation task.

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 is free of redundant information. Every word adds value, making it highly concise.

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?

The tool has 8 parameters, no output schema, and a complex domain (cascade risk modeling). The description only states the high-level goal and does not explain the expected output, how to interpret results, or how the tool handles edge cases. An agent would likely need additional information to invoke it correctly with confidence.

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%, and each parameter has a clear description (e.g., 'weighted adjacency matrix describing link strengths'). The tool description itself adds no parameter-specific semantics, but the baseline of 3 applies because the schema fully documents parameters.

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 uses a specific verb ('estimate') and resource ('shock propagation and resilience') with a clear data source ('adjacency-linked entities'). It distinguishes from sibling tools like waveguard_instability by emphasizing cascade dynamics and graph structure.

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 mention of when to use this tool versus alternatives. The description does not provide usage context, prerequisites, or exclusions. With 18 sibling tools, the lack of guidance leaves the agent to infer applicability solely from the tool name and one-line description.

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