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calebn
by calebn

low_audibility_words_tool

Identify and list words that are too quiet relative to the mix, helping podcast editors find and fix low-audibility dialogue.

Instructions

List words too quiet to hear relative to the mix.

Input Schema

NameRequiredDescriptionDefault
speakerNo
track_idNo
project_pathNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.1.1
    • addedInput schema / properties / project_path / anyOf
      Added value: +[
      +  {
      +    "type": "string"
      +  },
      +  {
      +    "type": "null"
      +  }
      +]
    • addedInput schema / properties / project_path / default
      Added value: +null
    • removedInput schema / properties / project_path / type
      Removed value: -"string"
    • removedInput schema / required
      Removed value: -[
      -  "project_path"
      -]
  2. First observedv0.1.0

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It says nothing about whether this is a read-only listing, how many results come back, whether results are paginated, or how the speaker/track_id/project_path filters change the scope.

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?

One efficient, front-loaded sentence with no filler. It is not overly long, though the brevity comes at the cost of the missing detail noted elsewhere.

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?

An output schema exists, so return values need no explanation. However, for a tool with three completely undocumented parameters, no annotations, and no usage context, a single sentence leaves the agent without enough to call it correctly.

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

Parameters1/5

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

Schema description coverage is 0% and all three parameters (speaker, track_id, project_path) are undocumented nullable strings. The description adds no parameter meaning at all, so an agent cannot tell whether these are required scoping inputs or optional narrowing filters.

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 clear verb (List) and resource (words) with a defined scope: 'too quiet to hear relative to the mix'. This lets an agent distinguish it from nearby siblings like low_confidence_words_tool or bleed_words_tool, though the description never names those alternatives explicitly.

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?

No when-to-use guidance, no prerequisites, and no mention of related tools such as flagged_words_tool or audibility_map_tool. The agent must infer the pipeline stage from the phrase 'relative to the mix' alone.

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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