WaveGuard

waveguard_action_surface

Read-onlyIdempotent

Score candidate actions and extract robust action zones.

Input Schema

TableJSON Schema

Name	Required	Description
`training`	Yes	2+ baseline normal samples used to define the reference profile.
`field_level`	No	0 = real scalar field, 1 = complex field.
`sensitivity`	No	Anomaly sensitivity multiplier (default: 1.0).
`action_tests`	Yes	1+ candidate actions/scenarios to score against baseline.
`encoder_type`	No	Optional encoder override. Omit to auto-detect.
`action_labels`	No	Optional labels for each action variant.

Schema Changelog

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

First observedJul 28, 2026

TDQS

B3.1/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description 'score... and extract' does not contradict these and adds no additional behavioral traits such as dependencies, side effects, or rate limits.

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 a single, front-loaded sentence with no wasted words. It is appropriately concise for a simple tool, though it could include a bit more structure without becoming verbose.

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 has 6 parameters and no output schema, the description is too sparse. It does not explain what 'robust action zones' are, what the output format looks like, or how the parameters interact. The annotations help with safety but not with functional completeness.

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 all 6 parameters described adequately. The tool description adds no extra meaning about parameters beyond what the schema provides, so it meets the baseline of 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 clearly states a specific action: 'Score candidate actions and extract robust action zones.' It uses a specific verb and resource, but 'robust action zones' is jargon not explained, slightly reducing clarity. It does not explicitly distinguish from sibling tools, but the purpose is focused.

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 the many sibling tools (e.g., waveguard_compare, waveguard_counterfactual). There are no context cues, exclusions, or alternatives mentioned, leaving usage ambiguous.

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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