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wavelet_transform

Decompose a signal into discrete wavelet coefficients, save full arrays to an NPZ file, and return per-level lengths, energies, and previews for signal analysis.

Instructions

Decompose a signal with a discrete wavelet transform (pywt.wavedec).

The maximum level is used when ``level`` is omitted. Full coefficient arrays
are saved to a .npz file; the return value carries lengths, energies and a
short preview of each level.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNosymmetric
levelNo
signalYes
waveletNodb4

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses a side effect (full coefficient arrays written to a .npz file) and the shape of the return value, plus the default-level behavior. It does not say where the file lands, whether it overwrites, or what permissions are needed, so the disclosure is partial.

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?

Three short lines, front-loaded with the core operation and followed by the two facts an agent most needs (default level, file output). Minimal waste, though the parenthetical library reference is slightly incidental.

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?

With no output schema and no annotations, the description does well to sketch the return (lengths, energies, preview per level) and the .npz side effect. It still leaves the ambiguous `signal` string form and the file destination unexplained, so an agent cannot fully predict the call's effects.

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

Parameters2/5

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

Schema description coverage is 0% across 4 parameters, so the description must compensate. It only clarifies `level` (max level used when omitted); `mode`, `wavelet` (default db4), and especially `signal` — which accepts either an array or a string, presumably a path — are left entirely undocumented.

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?

States a specific verb and resource — "Decompose a signal with a discrete wavelet transform" — and even names the underlying routine (pywt.wavedec), which pins down the exact semantics. It does not differentiate from the sibling wavelet_denoise, which an agent could easily confuse with this transform tool.

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?

There is no explicit guidance on when to choose this over wavelet_denoise, fft_analysis, stft, or time_series_decompose. Usage is only implied by the verb "decompose," and no prerequisites or exclusions are stated.

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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