WaveGuard

waveguard_compare

Read-onlyIdempotent

Compare two data items for structural similarity using physics-based fingerprints. Returns cosine similarity (0–1) and Euclidean distance. Use for duplicate detection, behavioral matching, drift analysis, or checking if two tokens/wallets/contracts are structurally similar.

Cosine similarity > 0.95 = very similar. < 0.80 = structurally different.

Input Schema

TableJSON Schema

Name	Required	Description
`data_a`	Yes	First data item to compare.
`data_b`	Yes	Second data item to compare (same type as data_a).
`encoder_type`	No	Data encoder. Omit to auto-detect.

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and openWorldHint=true. The description adds value by explaining the output (cosine similarity and Euclidean distance) and providing interpretation thresholds, which are not present in annotations.

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 concise with three well-organized sentences: function overview, use cases, and output interpretation. No unnecessary information, front-loaded with core purpose.

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

Completeness5/5

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

For a comparison tool with good annotations and full schema coverage, the description adequately covers purpose, usage, and output interpretation. It is complete for an AI agent to understand and invoke correctly.

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 descriptions for all parameters. The description does not add significant new meaning beyond the schema; it mentions comparing 'two data items' but does not elaborate on parameter details or constraints.

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 clearly states it compares two data items for structural similarity using physics-based fingerprints, listing specific use cases. The verb 'compare' and resource 'data items' are specific, and the tool is well-differentiated from siblings through its unique functionality.

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

Usage Guidelines3/5

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

The description provides example use cases (duplicate detection, behavioral matching, drift analysis) but does not explicitly state when not to use the tool or mention alternative tools like waveguard_fingerprint. Guidance is implied, not explicit.

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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