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Analyze Formants

analyze_formants

Extracts F1/F2/F3 formant frequencies per step and the nearest vowel from vocals, to reveal sung content and match vocoder or formant sounds.

Instructions

Vowel view of a voice: LPC formants F1/F2/F3 per step and the nearest vowel (max 8 bars). Use it to see what a vocal part is saying/singing and to match a vocoder / formant sound to it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
barsNo
sourceNoref:vocals
projectYes
steps_per_beatNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

B3.4/5.0
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 does disclose real behavioral traits: the LPC method, per-step resolution, and a hard cap of 8 bars. However, it says nothing about read-only vs mutating behavior, whether it needs rendered audio, or what the defaults do — significant gaps for a zero-annotation tool.

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?

Two dense sentences with the output shape front-loaded and usage second; every clause carries information. The opening "Vowel view of a voice" is slightly colloquial but quickly resolves into concrete detail.

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?

An output schema exists, so return values need not be re-explained, and the description's framing of the analysis is adequate for selection. But with 0% schema coverage on four parameters, the definition leaves too much about inputs unspecified to be fully complete.

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 hints at the bars limit ("max 8 bars") and per-step resolution ("per step"), but never explains the `source` ref format, `project`, or the structure of the `bars` array, leaving most parameters opaque.

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 names a specific analysis output (LPC formants F1/F2/F3 per step plus nearest vowel), which is a concrete verb+resource and clearly distinct from siblings like analyze_spectrum or analyze_timbre. It stops short of explicitly naming the alternatives it is not.

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?

"Use it to see what a vocal part is saying/singing and to match a vocoder / formant sound to it" gives clear context for when the tool applies. It offers no exclusions or named alternatives, but the use case is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.