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@aiwerk/mcp-server-elevenlabs

by AIWerk
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TypeScript
Hybrid

text_to_speech_full

Convert text into audio with ElevenLabs voices and models. Returns MP3 bytes or saves them to a file, so you can generate voice output from written content.

Instructions

Text To Speech Spends ElevenLabs credits. Returns audio/mpeg bytes; pass output_path to save them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
textYesThe text that will get converted into speech.
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.
voice_idYesVoice ID to be used, you can use https://api.elevenlabs.io/v1/voices to list all the available voices.
next_textNo
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 and WAV formats with 44.1kHz sample rate requires you to be
previous_textNo
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.
use_pvc_as_ivcNoIf true, we won't use PVC version of the voice for the generation but the IVC version. This is a temporary workaround for higher latency in PVC versions.
voice_settingsNo
next_request_idsNo
previous_request_idsNo
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.
optimize_streaming_latencyNoYou can turn on latency optimizations at some cost of quality. The best possible final latency varies by model. Possible values: 0 - default mode (no latency optimizations) 1 - normal latency optimizations (about 50% of possible latency improvement of option 3) 2 - strong latency optimizations (abou
apply_language_text_normalizationNoThis parameter controls language text normalization. This helps with proper pronunciation of text in some supported languages. WARNING: This parameter can heavily increase the latency of the request. Currently only supported for Japanese.
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 cover the safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false). The description adds genuinely non-structured context: the call spends ElevenLabs credits (a cost side effect) and returns audio/mpeg bytes. It stops short of noting that omitting output_path falls back to inline base64 with size limits, which the schema covers but the description does not reinforce.

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 cost warning front-loaded. The first sentence reads slightly awkwardly ('Text To Speech Spends ElevenLabs credits') but wastes nothing.

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 an 18-parameter generation tool with no output schema, the description covers the return type and the save-to-disk path, which is the minimum an agent needs. It omits the credit-cost magnitude, model/voice prerequisites, and the inline-base64 fallback behavior, leaving notable gaps for a tool this complex.

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?

Schema description coverage is only 56% across 18 parameters, and the description adds meaning for just one of them (output_path), which the schema already documents in more detail. The many undocumented parameters such as next_text, previous_text, seed, and pronunciation_dictionary_locators get no help from the description.

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 verb+resource (text to speech) and adds the key differentiator that this returns audio/mpeg bytes rather than a stream. However, it never distinguishes itself from close siblings such as text_to_speech_stream, text_to_speech_full_with_timestamps, or text_to_voice, so an agent still has to guess why 'full' is the right choice.

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 description notes that passing output_path saves bytes, which is a usage hint, but gives no when-to-use or when-not-to-use guidance. It never says whether to prefer this over the streaming or timestamped variants, which are the obvious alternatives in the sibling list.

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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