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ElevenLabs

by io.github.mcp-dir

elevenlabs_text_to_speech_full

Text To Speech. Converts text into speech using a voice of your choice and returns audio.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
textYes
accountNo
model_idNo
voice_idYes
next_textNo
language_codeNo
output_formatNo
previous_textNo
enable_loggingNo
use_pvc_as_ivcNo
voice_settingsNo
next_request_idsNo
previous_request_idsNo
apply_text_normalizationNo
optimize_streaming_latencyNo
apply_language_text_normalizationNo
pronunciation_dictionary_locatorsNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.4/5.0
Behavior2/5

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

The description only adds that the tool 'returns audio,' which is useful since there is no output schema. However, it does not disclose behavior around model selection, output format defaults, streaming/latency, voice settings, logging, or the side effects of a non-read-only generation operation.

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

Conciseness3/5

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

The description is very short and front-loaded, with no wasted wording in the second sentence. However, the first sentence 'Text To Speech' is redundant with the tool name, and the overall size is too small for a tool with this many parameters.

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?

This is a high-complexity tool with 18 parameters, no output schema, and sparse annotations, yet the description offers only a basic sentence. It omits essential context such as output format behavior, voice settings, model selection, advanced streaming/text-chaining parameters, and expected return structure.

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

Parameters1/5

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

With 18 parameters and 0% schema description coverage, the description must compensate, but it only vaguely maps to text and voice_id. The 16 other parameters—output_format, voice_settings, seed, model_id, language_code, previous/next_text, request IDs, and more—are left completely unexplained.

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?

The description clearly states the core operation: converting text into speech with a chosen voice and returning audio. It is specific enough to identify this as a TTS tool, though it does not explain how 'full' differs from sibling tools like elevenlabs_text_to_dialogue or elevenlabs_text_to_voice_preview_stream.

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?

No guidance is provided about when to use this tool instead of the many sibling ElevenLabs tools. It does not mention use cases, alternatives, prerequisites, or when the advanced parameters would be relevant.

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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TDQS

C2.4/5.0
Disambiguation2/5

There are many tools with overlapping purposes, such as multiple voice retrieval tools (get_voice_by_id, get_voices, get_user_voices_v2, get_library_voices) and several dubbing transcript segment editors with only subtle naming differences. The inclusion of platform-level tools (authenticate, connect, marketplace) alongside ElevenLabs API tools further blurs boundaries.

Naming Consistency1/5

Naming is highly inconsistent. Most tools have the 'elevenlabs_' prefix, but some do not (authenticate, connect, marketplace, report_bug, show_version, toolkit_info). Several tools have truncated/random suffix names (e.g., elevenlabs_dubbing_target_transcript_segmen_b565e6, elevenlabs_get_pronunciation_dictionary_ver_45baf2), and one tool is in Portuguese (elevenlabs_list_accounts). This mixture of conventions and languages makes the pattern unpredictable.

Tool Count1/5

With 155 tools, the server is extremely bloated. It mixes a comprehensive ElevenLabs API surface with unrelated MCP platform tools (marketplace, authenticate, report_bug, etc.) that belong in a separate toolkit. This is a severe mismatch between the apparent purpose (ElevenLabs audio services) and the sheer number of tools.

Completeness3/5

The ElevenLabs-specific tools cover a wide range of operations (text-to-speech, voice management, dubbing, pronunciation dictionaries, Studio projects, workspace administration, order management), making it fairly complete for those domains. However, the inclusion of unrelated platform tools and the lack of a clear focus mean that an agent would have difficulty navigating this large surface, and some operations like music finetuning or speech engines appear only partially covered.

Resources