WaveGuard

waveguard_phase_coherence

Read-onlyIdempotent

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

Input Schema

TableJSON Schema

Name	Required	Description
`test`	Yes	1+ candidate samples to evaluate for phase coherence and entropy.
`training`	Yes	2+ baseline normal samples for reference coherence metrics.
`field_level`	No	Field representation level. Default 1 for phase-aware analysis.
`sensitivity`	No	Anomaly sensitivity multiplier (default: 1.0).
`encoder_type`	No	Optional encoder override. Omit to auto-detect.

Schema Changelog

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

First observedJul 28, 2026

TDQS

A3.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds context by specifying what is measured (coherence, entropy, collapse-risk indicators), which aligns with the annotations and provides useful behavioral insight.

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 with no wasted words. It effectively communicates the tool's purpose succinctly.

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

Completeness3/5

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

Given the absence of an output schema, the description does not explain what the tool returns, such as a risk score or coherence value. It is adequate for a read-only tool with rich annotations but lacks completeness for an agent to fully understand the output.

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%, so the description adds no new meaning beyond the field descriptions. The description's mention of 'candidate data' and 'baseline normal samples' is implicit and not detailed beyond the schema.

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 clearly states the tool measures coherence/entropy and collapse-risk indicators for candidate data. It uses a specific verb and resource, but could better distinguish from similar sibling tools like waveguard_instability.

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 such as waveguard_health or waveguard_instability. There is no mention of when to use or when not to use.

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

A3.9/5.0

Disambiguation5/5

Each tool has a clear, distinct purpose. While tools like waveguard_scan and waveguard_scan_timeseries both detect anomalies, their descriptions clearly differentiate structured data from time-series data. Similarly, token risk, volume check, and price manipulation tools address separate aspects of token analysis, avoiding overlap.

Naming Consistency5/5

All tools follow the consistent pattern 'waveguard_<descriptive_name>' in snake_case. The names are descriptive and predictable, making it easy to infer functionality from the name alone. No mixed conventions or vague names are present.

Tool Count4/5

19 tools is on the higher side but still appropriate given the broad domain of crypto analytics, anomaly detection, and market data. Each tool serves a specific purpose, and the count is justified by the comprehensive feature set. However, a few tools could potentially be merged without loss of clarity.

Completeness4/5

The tool set covers a wide range of functionalities: health check, market data fetching, fingerprinting, similarity comparison, anomaly detection (structured and time-series), risk assessment (token, price manipulation, volume, wallet), counterfactuals, and interaction matrices. Some minor gaps exist, such as missing data export or visualization tools, but core workflows are well-supported.

Resources

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