elevenlabs-mcp

by neomorrison

Overview Schema Source Related Servers Discussions Admin

Python

Local

speech_to_text

Transcribe audio files to text, with optional speaker diarization and language hints.

Instructions

Transcribe an audio file to text (ElevenLabs Scribe).

Args: audio_file_path: path to the audio file to transcribe. model_id: STT model id (default "scribe_v1"). language_code: optional ISO language hint. diarize: annotate which speaker said what. tag_audio_events: tag non-speech events like (laughter). num_speakers: hint for the expected number of speakers.

Returns the transcription result as JSON (text, language, words with timings).

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_file_path": "<audio_file_path>"
    },
    "name": "speech_to_text"
  }
}

Input Schema

Name	Required	Default
`diarize`	No
`model_id`	No	scribe_v1
`num_speakers`	No
`language_code`	No
`audio_file_path`	Yes
`tag_audio_events`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 8, 2026

TDQS

A4.1/5.0

Behavior3/5

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 outlines the transcribe operation and return format, but omits details like file size limits, audio format requirements, or potential side effects (e.g., cost implications). Basic behavior is covered, but operational constraints are missing.

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 well-structured with a concise summary, a clear Args section, and a Returns line. Each sentence is necessary and adds value, with no redundancy or fluff.

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?

Given 6 parameters and no annotations, the description covers purpose, all parameters, and return format. It misses some operational prerequisites (e.g., file existence, format support) and error conditions, but the output schema likely handles return details. The description is adequately complete for an 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.

Parameters5/5

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

The input schema has 0% description coverage, but the description explains each parameter in detail: audio_file_path (path), model_id (default), language_code (ISO hint), diarize (speaker annotation), tag_audio_events (non-speech events), num_speakers (hint). This fully compensates for the lack of schema descriptions, providing clear semantics.

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 explicitly states 'Transcribe an audio file to text (ElevenLabs Scribe).' This is a specific verb-resource combination that clearly conveys the tool's function and distinguishes it from sibling tools like text_to_speech (text to audio) and audio_isolation (separate audio).

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

Usage Guidelines3/5

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

The description lists parameters but does not provide guidance on when to use this tool over alternatives such as forced_alignment or speech_to_speech. The usage context is implied by the purpose, but no explicit when-to-use or when-not-to-use advice is given.

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