Sound Generation. Turn text into sound effects for your videos, voice-overs or video games using the most advanced sound effects models in the world.
Bulk support: accepts model_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| loop | No | ||
| text | Yes | ||
| account | No | ||
| model_id | No | ||
| model_ids | No | ||
| output_format | No | ||
| duration_seconds | No | ||
| prompt_influence | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint=false, idempotentHint=false) already signal a non-idempotent generation operation, and the description is consistent — no contradiction. The 'accepts model_ids for batched execution' note adds one behavioral trait beyond annotations, but the description does not disclose output format defaults, cost/credit implications, or failure behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded, with the purpose stated in the first line. However, 'using the most advanced sound effects models in the world' is marketing filler that earns no operational value, while the bulk-support note is useful but underexplained.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 8 parameters, no output schema, and no per-parameter documentation, three sentences are insufficient. The description does not state what the tool returns (audio?), whether model_id and model_ids are mutually exclusive or combined, or default behaviors for optional parameters like loop and output_format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description names only model_ids (for batched execution). The other seven parameters — text, loop, account, model_id, output_format, duration_seconds, and prompt_influence — receive no semantic explanation, leaving the agent to guess at their meaning and relationships.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb+resource: 'Turn text into sound effects', and the use cases (videos, voice-overs, video games) distinguish it from sibling generation tools like elevenlabs_text_to_speech_full and elevenlabs_text_to_dialogue. The marketing superlative 'most advanced sound effects models in the world' is fluff but does not obscure the core purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage via target contexts ('for your videos, voice-overs or video games'), and the 'Bulk support' note hints at a batching scenario. However, it offers no explicit when-to-use/when-not-to-use guidance or comparison against sibling sound-generation alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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 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.
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.
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.