Skip to main content
Glama
Sign In
waveform-MCP

waveform-MCP

by jarmstrong158
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

voice_lead

Voice a target chord with minimal motion from the previous voicing, using SATB-style voice leading. Returns the new voicing as MIDI pitches.

Instructions

Voice the next chord with minimal voice motion from the previous voicing. Standard SATB-style voice leading: each voice moves the smallest interval to a target chord tone. Returns the new voicing as a list of MIDI pitches (same length as prev_voicing).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prev_voicing": "<prev_voicing>",
      "next_chord": "<next_chord>"
    },
    "name": "voice_lead"
  }
}

Input Schema

NameRequiredDescriptionDefault
next_chordYesTarget chord tones (any octave; will be re-voiced)
prev_voicingYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool performs minimal voice motion, uses SATB-style, and returns a list of MIDI pitches of the same length. However, it does not mention behavior on failure (e.g., if the chord cannot be voiced) or any constraints like voice ranges or octave handling.

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 two sentences, front-loaded with the purpose and key behavior. It is concise with no redundant phrasing, and every sentence adds value.

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?

The tool is a simple function with two parameters, and the description explains the return format. However, it does not cover edge cases such as mismatched voice counts or impossible voicings, and it lacks any guidance on error handling. Given no output schema and no annotations, this is a notable gap.

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

Parameters4/5

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

The schema only describes next_chord; prev_voicing is undocumented. The description compensates by clarifying that prev_voicing is the previous voicing and that next_chord is the target chord tones, and it implies both are MIDI pitches via the return description. This adds meaning beyond the schema.

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 the verb 'Voice' and the resource 'the next chord', with the specific goal of minimal voice motion. It distinguishes itself from siblings like theory_voice_leading_rules by being an actionable transformation rather than a rule reference. The return format is also specified.

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?

The description gives no explicit guidance on when to use this tool versus alternatives. It does not mention when it would be appropriate, when not to use it, or any sibling tool that might be preferred for different scenarios (e.g., theory_voice_leading_rules for rules).

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

Deploy Server

Other Tools