Whissle MCP Server

diarize_speech

Transcribe audio files into text while identifying different speakers, saving the output with speaker labels for clear conversation analysis.

Instructions

Convert speech to text with speaker diarization and save the output text file to a given directory. Directory is optional, if not provided, the output file will be saved to $HOME/Desktop.

⚠️ COST WARNING: This tool makes an API call to Whissle which may incur costs. Only use when explicitly requested by the user.

Args:
    audio_file_path (str): Path to the audio file to transcribe
    model_name (str, optional): The name of the ASR model to use. Defaults to "en-NER"
    max_speakers (int, optional): Maximum number of speakers to identify
    boosted_lm_words (List[str], optional): Words to boost in recognition
    boosted_lm_score (int, optional): Score for boosted words (0-100)
    output_directory (str, optional): Directory where files should be saved.
        Defaults to $HOME/Desktop if not provided.

Returns:
    TextContent with the diarized transcription and path to the output file.

Input Schema

TableJSON Schema

Name	Required	Default
`audio_file_path`	Yes
`model_name`	No	en-NER
`max_speakers`	No
`boosted_lm_words`	No
`boosted_lm_score`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Feb 2, 2026

TDQS

A4.6/5.0

Behavior4/5

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 effectively describes key behaviors: it's a write operation (saves output files), has cost implications (API call to Whissle), and specifies default behavior (output directory defaults to $HOME/Desktop). It doesn't mention rate limits or error handling, but covers the most critical aspects for a cost-incurring tool.

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?

The description is well-structured with a clear purpose statement, cost warning, parameter explanations, and return information. Every sentence adds value, though the parameter section is somewhat lengthy. It's appropriately sized for a tool with 5 parameters and no schema descriptions, but could be slightly more concise in the Args section.

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

Completeness5/5

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

Given the tool's complexity (5 parameters, cost implications, file output) and the absence of annotations, the description provides comprehensive context. It covers purpose, usage warnings, parameter semantics, and return values (TextContent with transcription and file path). The presence of an output schema means it doesn't need to detail return structure, and it addresses all critical aspects for the agent to use the tool correctly.

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 description adds significant semantic value beyond the input schema, which has 0% description coverage. It explains what each parameter does (e.g., 'model_name' specifies the ASR model, 'max_speakers' limits speaker identification), provides defaults, and clarifies optionality. The only gap is that 'boosted_lm_words' and 'boosted_lm_score' could be better explained, but overall it compensates well for the schema's lack of descriptions.

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?

The description clearly states the tool's purpose with specific verbs ('convert speech to text with speaker diarization') and resources ('audio file'), and distinguishes it from sibling tools like 'speech_to_text' by emphasizing speaker diarization. It goes beyond the tool name to explain the core functionality.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance with a cost warning ('⚠️ COST WARNING: This tool makes an API call to Whissle which may incur costs. Only use when explicitly requested by the user'), which clearly indicates when to use (only when explicitly requested) and when to avoid (due to costs). This helps the agent choose alternatives like 'speech_to_text' for non-diarized transcription.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server