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@aiwerk/mcp-server-elevenlabs

by AIWerk
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TypeScript
Hybrid

text_to_speech_full_with_timestamps

Convert text into speech audio and return timestamps to sync captions, subtitles, or transcripts. Uses ElevenLabs credits.

Instructions

Text To Speech With Timestamps Spends ElevenLabs credits.

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
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_idsNoA list of request_id of the samples that come after this generation. next_request_ids is especially useful for maintaining the speech's continuity when regenerating a sample that has had some audio quality issues. For example, if you have generated 3 speech clips, and you want to improve clip 2, pas
previous_request_idsNoA list of request_id of the samples that were generated before this generation. Can be used to improve the speech's continuity when splitting up a large task into multiple requests. The results will be best when the same model is used across the generations. In case both previous_text and previous_r
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_locatorsNoA list of pronunciation dictionary locators (id, version_id) to be applied to the text. They will be applied in order. You may have up to 3 locators per request

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

D1.9/5.0
Behavior3/5

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

Annotations already disclose readOnlyHint=false, idempotentHint=false and openWorldHint=true. The description earns credit for adding that the call consumes ElevenLabs credits, a cost/rate-limit fact annotations do not convey, but it says nothing about the generation being non-reversible or the timestamp payload.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Only a single ungrammatical sentence fragment, and it is under-specified rather than concise. It is not so much wasteful as absent, which is a different structural failure.

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

Completeness1/5

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

For a 17-parameter, non-idempotent, credit-consuming generation tool with no output schema, one fragment is wholly inadequate. Nothing tells the agent how timestamps are returned or what a successful call produces.

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 17 parameters and 71% schema description coverage, the schema does most of the work, but the description contributes zero parameter meaning. Nothing explains required voice_id/text, the timestamp-relevant options, or the continuity parameters (previous/next_request_ids).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description restates the tool's own name ('Text To Speech With Timestamps') without a distinct verb or scoping statement. It adds only a credit-cost note, so an agent learns nothing about what differentiates it from text_to_speech_full or text_to_speech_stream_with_timestamps.

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

Usage Guidelines1/5

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

No when-to-use, when-not-to-use, or alternative-tool guidance is given, despite several closely named siblings (text_to_speech_full, text_to_speech_stream, text_to_speech_stream_with_timestamps). The credit remark hints at cost but does not route the agent to any alternative.

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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