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@aiwerk/mcp-server-elevenlabs

by AIWerk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

text_to_dialogue_stream

Generate multi-voice dialogue audio by converting text inputs with voice IDs into streaming audio bytes; save via output_path or use inline.

Instructions

Text To Dialogue (Multi-Voice) Streaming Spends ElevenLabs credits. Returns audio/mpeg bytes; pass output_path to save them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
inputsYesA list of dialogue inputs, each containing text and a voice ID which will be converted into speech. The maximum number of unique voice IDs is 10. For reliable generation, keep the total character count across all `inputs[].text` values at or below 2,000 characters per request. Longer requests can te
model_idNoIdentifier of the model that will be used, you can query them using GET /v1/models. The model needs to have support for text to speech, you can check this using the can_do_text_to_speech property.
settingsNo
output_pathNoWhere to write the returned bytes. Relative paths resolve against ELEVENLABS_OUTPUT_DIR. Omit it to get the data inline as base64 (small files only).
language_codeNo
output_formatNoOutput format of the generated audio. Formatted as codec_sample_rate_bitrate. So an mp3 with 22.05kHz sample rate at 32kbs is represented as mp3_22050_32. MP3 with 192kbps bitrate requires you to be subscribed to Creator tier or above. PCM with 44.1kHz sample rate requires you to be subscribed to Pr
enable_loggingNoWhen enable_logging is set to false zero retention mode will be used for the request. This will mean history features are unavailable for this request, including request stitching. Zero retention mode may only be used by enterprise customers.
apply_text_normalizationNoThis parameter controls text normalization with three modes: 'auto', 'on', and 'off'. When set to 'auto', the system will automatically decide whether to apply text normalization (e.g., spelling out numbers). With 'on', text normalization will always be applied, while with 'off', it will be skipped.
pronunciation_dictionary_locatorsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior4/5

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

Annotations already declare it is a non-read-only, open-world, non-idempotent operation, and the description usefully adds that it consumes credits and returns audio/mpeg bytes. These two facts (billing impact and return media type) are not derivable from the annotations and materially affect invocation.

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?

Two short sentences with no filler, and the credit-cost warning is placed early. The phrasing 'Text To Dialogue (Multi-Voice) Streaming Spends ElevenLabs credits' is slightly run-on, but the content is tightly written.

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

Completeness3/5

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

For a 10-parameter generation tool with no output schema, the description covers cost, return type, and persistence, but omits the dialogue input format constraints, the meaning of streaming, and voice/model selection context. Adequate as a minimum but leaves gaps an agent would hit when constructing inputs.

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

Parameters2/5

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

With 10 parameters at 60% schema coverage, roughly four parameters rely on the description for meaning, yet it only restates the output_path behavior that the schema already documents. Nothing is added about inputs[], seed, model_id, language_code, settings, or pronunciation_dictionary_locators.

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?

States a specific action (stream multi-voice text-to-dialogue audio) and clarifies it is the streaming variant, which separates it from text_to_dialogue and text_to_dialogue_full_with_timestamps. However, it does not explicitly contrast itself with those siblings, so an agent still has to infer the difference.

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?

The only guidance is a cost warning ('Spends ElevenLabs credits'). There is no statement of when to pick this over text_to_dialogue, text_to_dialogue_stream_with_timestamps, or text_to_speech_stream, and no prerequisites or exclusion conditions are given.

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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