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Detect fillers, pauses, mouth noise, hum

cast_analyze
Read-onlyIdempotent

Runs Cast's cleanup analysis on a recording: filler words (um/uh/like…), long silences, mouth clicks, background noise and mains hum. Free. Returns counts and the markers you can turn into cuts with cast_plan_cuts. Sensitivities are 0–100 (default 50). Runs as a background job; waits up to wait_seconds and otherwise returns a task_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNo
detect_mouthNo
detect_noiseNo
wait_secondsNo
audio_file_idYes
detect_fillersNo
detect_silencesNo
filler_sensitivityNo
silence_sensitivityNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations cover safety (readOnly/idempotent/openWorld). Description adds substantial context annotations cannot: 'Free', runs as background job, waits up to wait_seconds then returns task_id, returns counts plus markers. This is real operational guidance for an agent scheduling work and handling async responses.

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?

Roughly four short sentences, each carrying distinct payload: purpose, cost, output, parameter semantics, async behavior. Front-loaded with the core action.

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

Completeness4/5

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

No output schema, yet the description states what is returned (counts and markers). Async behavior is covered. Minor gap: does not tie result consumption to cast_get_task explicitly, though task_id is named.

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 description coverage is 0%, so description must compensate. It explains sensitivity scale (0–100, default 50) and defines wait_seconds behavior and the task_id fallback. Does not cover all 9 params (e.g., language, individual detect_* toggles), leaving gaps, but the high-value semantics are present.

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?

States a specific verb+resource: analysis of a recording returning filler/silence/noise/hum markers. Distinguishes itself from cast_transcribe (text) and cast_plan_cuts (turning markers into cuts) by naming the handoff.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Describes the workflow handoff to cast_plan_cuts, indicating when this step is used. No explicit when-not or scope limits, but the pipeline position is clear.

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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