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calebn
by calebn

join_label_tool

Records a pass/fail label for an audio join to train the join-quality ranker, enabling better join decisions in podcast editing.

Instructions

Record a pass/fail label for a join to train the join-quality ranker.

Input Schema

NameRequiredDescriptionDefault
noteNo
playNo
cut_endNo
speakerNo
verdictYes
join_secYes
timebaseNosource
track_idNo
cut_startNo
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"
    • changedInput schema / required
      Previous value: -[
      -  "project_path",
      -  "join_sec",
      -  "verdict"
      -]New value: +[
      +  "join_sec",
      +  "verdict"
      +]
  2. First observedv0.1.0

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, and it discloses very little: it implies a persisted write (a recorded label) but says nothing about required session state, whether labels are overwritable or reversible, accepted verdict values, or side effects on the ranker. This is a significant gap for a mutation-style tool with zero annotation coverage.

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?

A single front-loaded sentence with no filler or repetition; the recorded artifact and its purpose come first. It is brief rather than padded, though brevity here partly reflects under-specification.

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?

The tool has 10 parameters, no annotations, and no schema descriptions, yet the description is one vague sentence. An output schema exists so return values need not be explained, but the label vocabulary, required inputs, and the meaning of join_sec/timebase are all missing.

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 10 parameters, so the description must compensate. It hints at only two concepts implicitly — "join" (join_sec) and "pass/fail label" (verdict, whose valid string values remain undefined) — leaving note, play, cut_start, cut_end, speaker, timebase, track_id, and project_path entirely undocumented.

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 verb ("Record") and resource ("pass/fail label for a join") plus the downstream purpose (training the join-quality ranker), which separates it from adjacent tools like record_marker_tool, join_quality_tool, and join_qa_sweep_tool. It is clear but stops short of explicitly contrasting itself with those siblings.

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 when-to-use or when-not-to-use guidance and no named alternative; an agent is left to infer that this is a feedback-labeling call. The phrase "to train the join-quality ranker" hints at intent but gives no prerequisites (e.g. an active session or an existing join) or conditions that select this tool over join_quality_tool / join_qa_sweep_tool.

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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