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The Mechanical Ear

The Mechanical Ear

by luchtan-GIT
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Python
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get_dequantization_skill

Fetches Surgical Context De-Quantization SKILL.md guidance to break autoregressive lock-in and induction-head complacency without clearing context by directing agents to an unquantized MP3.

Instructions

Get the Music For Robots Surgical Context De-Quantization SKILL.md.

Breaks autoregressive lock-in and induction-head complacency without wiping or compacting the context window, by instructing the agent to fetch and listen to a compact, unquantized .mp3 composition that breaks prediction without breaking coherence (npx skills add luchtan-GIT/m4r-data).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_dequantization_skill"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.0

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose that the tool does not wipe or compact the context window and mentions fetching/listening to an .mp3 plus running an npx command, but it does not clarify permissions, side effects, whether this is read-only, or what the returned skill actually does.

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 first sentence front-loads the core action reasonably well, but the second sentence is dense with jargon and an embedded command, reducing clarity. It is not excessively long, but several phrases do not earn their place as operational guidance.

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

Completeness2/5

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

Although there is an output schema and no parameters, the tool's concept is unusual enough that the description should explain what a skill retrieval means operationally and when to invoke it. The current text leaves the agent with a metaphorical explanation, an ambiguous mp3-fetching instruction, and no sibling context.

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 tool has zero parameters, so there is no parameter semantics burden. Baseline 4 is appropriate because the schema covers the empty argument object and the description has no parameters to explain.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description does specify a verb and resource: getting the 'Music For Robots Surgical Context De-Quantization SKILL.md.' However, the purpose is wrapped in undefined jargon and a metaphorical explanation about breaking 'autoregressive lock-in,' and it gives no differentiation from siblings such as get_sections or calibrate_cognition.

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 when-to-use or when-not-to-use guidance, and no alternatives are named. The only hint is the vague claim that it 'breaks autoregressive lock-in and induction-head complacency,' which leaves the agent to infer the appropriate situation.

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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