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The Mechanical Ear

The Mechanical Ear

by luchtan-GIT
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Python
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get_critic_contrast

Calculates contrastive divergence between Vincent and Robach critiques for a track, returning regularization metrics and constraint weights to guide agent recommendations.

Instructions

Calculate the contrastive divergence vector between Vincent and Robach for a track.

Evaluates the tension between Vincent's deconstructive/fault-seeking critique (psychoacoustic loss, physical microtiming friction, clipping) and Robach's structural/formalist critique (harmonic axioms, tonal architecture, symmetry). Provides structured regularization metrics and negative constraint weights for agent decision-making and recommendation boundaries.

Args: slug: Track slug from list_tracks (e.g. "cathartic-callbacks", "binded-time")

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": {
      "slug": "<slug>"
    },
    "name": "get_critic_contrast"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.1.0

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are supplied, so the description carries the full burden. It never states that this is a read-only computation, whether it requires the track to already have both critics' reviews, what happens with an unknown slug, or whether the metrics are cached/derived on the fly. The only behavioral hint is 'Calculate', which weakly implies purity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening sentence is front-loaded and the Args block is tidy, but the middle paragraph explaining 'structured regularization metrics and negative constraint weights' largely restates what an existing output schema already covers, adding length without new actionable information.

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?

For a single-parameter analytical tool with an output schema, the description gives an adequate conceptual model of what is compared. It omits preconditions (must both critics' analyses exist?), failure modes for invalid slugs, and any indication of cost or determinism.

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?

Schema coverage is 0% for the single `slug` parameter, so the description must compensate — and it does, identifying the slug as coming from list_tracks and giving two concrete example formats. It could go further by noting the slug format rules or case sensitivity.

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?

Names a specific verb ('calculate') and a specific resource ('contrastive divergence vector between Vincent and Robach for a track'), and unpacks what each critic contributes, so an agent can tell this apart from generic analysis siblings like get_analysis or get_sections. It stops short of explicitly contrasting itself with those siblings by name.

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 phrase 'for agent decision-making and recommendation boundaries' implies when the output is useful, and the slug example ties input back to list_tracks. But there is no explicit when-to-use/when-not guidance and no statement of which sibling to prefer for related but different analysis.

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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