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Glama
MCP-Mirror

Whissle MCP Server

by MCP-Mirror
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
WHISSLE_AUTH_TOKENYesYour Whissle API authentication token (required)
WHISSLE_MCP_BASE_PATHNoBase directory for file operations (optional, defaults to user's Desktop)user's Desktop

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
speech_to_textA

Convert speech to text with a given model 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"
    timestamps (bool, optional): Whether to include word timestamps
    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 transcription and path to the output file.
diarize_speechA

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.
translate_textA

Translate text from one language to another.

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

Args:
    text (str): The text to translate
    source_language (str): Source language code (e.g., "en" for English)
    target_language (str): Target language code (e.g., "es" for Spanish)

Returns:
    TextContent with the translated text.
summarize_textA

Summarize text using an LLM model.

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

Args:
    content (str): The text to summarize
    model_name (str, optional): The LLM model to use. Defaults to "openai"
    instruction (str, optional): Specific instructions for summarization

Returns:
    TextContent with the summary.
list_asr_modelsB

List all available ASR models and their capabilities.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.8/5.0

Scored across 5 tools

Disambiguation4/5

Most tools have distinct purposes: diarize_speech adds speaker identification to transcription, speech_to_text is basic transcription, list_asr_models provides metadata, summarize_text and translate_text handle text processing. However, diarize_speech and speech_to_text share significant overlap in core transcription functionality, which could cause confusion about when to use each.

Naming Consistency5/5

All five tools follow a consistent verb_noun naming pattern with snake_case throughout: diarize_speech, list_asr_models, speech_to_text, summarize_text, and translate_text. The naming is predictable and follows the same grammatical structure across all tools.

Tool Count4/5

Five tools is reasonable for a speech/text processing server, though it feels slightly thin for comprehensive coverage. The count is appropriate for core functionality but could benefit from additional tools for more advanced operations like text analysis or audio preprocessing.

Completeness3/5

The server covers basic speech-to-text and text processing workflows, but has notable gaps. There's no way to edit or delete generated files, no batch processing capabilities, and no tools for audio preprocessing or quality assessment. While core transcription and text transformation are present, the surface feels incomplete for production workflows.

Resources