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@aiwerk/mcp-server-elevenlabs

by AIWerk
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TypeScript
Hybrid

text_to_speech_stream_with_timestamps

Streams text-to-speech audio with synchronized timestamps for real-time alignment. Consumes ElevenLabs credits per request.

Instructions

Text To Speech Streaming 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 with 44.1kHz sample rate requires you to be subscribed to Pr
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

C2.6/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, and destructiveHint=false, so the safety profile is known. The only behavioral fact added is 'Spends ElevenLabs credits', which is useful cost context, but the description says nothing about streaming semantics, timestamp delivery, partial output, or retention behavior that would matter for this specific variant.

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?

It is a single short sentence with no filler and the credit-cost note is front-loaded alongside the name. However, it is arguably too terse rather than concise-structuring useful content, and the sentence is essentially a name sandwich rather than a structured description.

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

Completeness2/5

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

For a 17-parameter, streaming, credit-consuming operation with no output schema, the description is insufficient. It omits streaming behavior, timestamp availability, tier/plan constraints mentioned in parameters (e.g., Creator tier for high-bitrate MP3), and return shape, while annotations cover only safety hints.

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

Parameters3/5

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

Schema description coverage is 53%, so several parameters (seed, next_text, previous_text, language_code, previous/next_request_ids, pronunciation_dictionary_locators) are undocumented in both places. The description supplies no parameter guidance at all, so it neither compensates for the gap nor exceeds the baseline for the documented half.

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

Purpose3/5

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

The description only restates the tool name ('Text To Speech Streaming With Timestamps') and appends a billing note. It conveys the core action (TTS streaming with timestamps) but adds no verb+resource detail beyond the title, and does not distinguish this streaming variant from text_to_speech_stream or text_to_speech_full_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 Guidelines2/5

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

There is no when-to-use guidance. The description does not tell the agent when to choose streaming-with-timestamps over streaming-without or the full-with-timestamps sibling, despite the tool being one of four near-identical TTS variants 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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