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identify_speakers

Identify which speaker is talking at each moment in an audio file. Supports auto-detection of speaker count or explicit specification.

Instructions

Identify who speaks when in audio.

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": {
      "audio_path": "<audio_path>"
    },
    "name": "identify_speakers"
  }
}

Input Schema

NameRequiredDescriptionDefault
audio_pathYesPath to the audio file
model_sizeNoWhisper model size. ALWAYS use tiny unless the user explicitly requests a different size. tiny is already highly accurate.
num_speakersNoNumber of speakers if known. Auto-detects if not set.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description is the only source of behavioral disclosure, but it only restates the high-level function. It does not mention output format, limitations, whether any model or download is required, or what 'who speaks when' returns. The schema provides model_size guidance, but the description itself adds no behavioral context.

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?

The description is a single front-loaded sentence with no filler, repetition, or irrelevant detail. It is optimally concise, even though more content could be added for completeness in other dimensions.

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?

This is a speaker-diarization tool with no output schema and no annotations, but the description does not describe the output format (e.g., speaker segments or timestamps) or any prerequisites. While the schema covers parameters well, the overall tool context is incomplete for an agent to know what result to expect.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description itself adds no parameter explanation beyond the schema, though the schema already includes useful guidance such as 'ALWAYS use tiny unless the user explicitly requests a different size.'

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 'Identify who speaks when in audio' uses a specific verb and resource, clearly indicating speaker diarization. It is distinct in meaning from sibling tools like transcribe_audio or separate_audio, but it does not explicitly name alternatives or scope, so it misses the top score.

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 guidance is provided on when to use this tool versus alternatives such as transcribe_audio or separate_audio. It neither states use cases, prerequisites, nor exclusions, just a functional one-liner.

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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