WaveGuard

waveguard_scan_timeseries

Read-onlyIdempotent

Detect anomalies in time-series data — use after pulling numeric metrics from monitoring APIs, financial data sources, IoT sensors, or spreadsheet columns. Send a single numeric array and specify a window size. Early windows define 'normal', recent windows are tested for anomalies.

Typical workflow: (1) Pull a column of numbers from Sheets, a Supabase time-series table, or a metrics API. (2) Pass the array here. (3) Get back which time windows are anomalous.

Examples:

Revenue monitoring: Pull monthly revenue from Sheets → detect anomalous months
Stock screening: Pull 90 days of closing prices → find unusual price windows
Server health: Pull response-time metrics → identify degradation windows
Sensor QA: Pull temperature readings from IoT API → flag sensor drift

Input Schema

TableJSON Schema

Name	Required	Description
`data`	Yes	Numeric time-series array, ordered chronologically. Should have at least 3x window_size data points.
`sensitivity`	No	Anomaly sensitivity (default: 1.0). Higher = more sensitive.
`window_size`	No	Number of data points per window (default: 10). Smaller windows detect finer-grained anomalies.
`test_windows`	No	Number of most recent windows to test (default: half of total windows). The rest are used as training (normal baseline).

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds algorithm detail: 'Early windows define normal, recent windows are tested for anomalies', which is helpful beyond annotations. No contradiction.

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?

Description is well-structured: purpose first, then parameter hints, then workflow, then examples. Concise enough but includes a useful typical workflow and examples. Could be slightly tighter but effectively communicates.

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

Completeness4/5

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

Given 4 parameters, 100% schema coverage, and no output schema, the description provides a complete picture of input requirements and algorithm behavior. It explains what the tool does and how to use it. Return format is not specified, but the examples imply a list of anomalous windows, which is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 4 parameters are described in schema (100% coverage). The description adds workflow context (e.g., 'Should have at least 3x window_size data points' for data, 'Higher = more sensitive' for sensitivity, 'Smaller windows detect finer-grained anomalies' for window_size). It explains the role of test_windows in dividing training vs. testing.

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?

Description clearly states 'Detect anomalies in time-series data'. It provides specific verb-resource pairing and distinguishes from sibling tools like waveguard_scan (general) and waveguard_trajectory_scan (trajectories). Examples (revenue, stock, server, sensor) reinforce the specific domain.

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

Usage Guidelines4/5

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

Explicitly says 'use after pulling numeric metrics from monitoring APIs, financial data, IoT sensors, or spreadsheet columns' and provides a typical workflow (pull → pass → get results). Lacks explicit when-not-to-use, but context is clear.

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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