Retrieve Voice Sample Visual Waveform. Retrieve the visual waveform of a voice sample.
Bulk support: accepts voice_ids, sample_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| voice_id | Yes | ||
| sample_id | Yes | ||
| voice_ids | No | ||
| sample_ids | 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 already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the description doesn't need to repeat those. The description adds the bulk support capability (voice_ids, sample_ids) which is beyond the schema's basic fields. However, it doesn't disclose return format or details about what the waveform data looks like.
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 two sentences, front-loaded with the purpose, and every sentence adds value. There is no redundancy or wasted words.
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?
The tool has 5 parameters with 0% schema description coverage, but the description covers the core resource and bulk support. It lacks detail on return format, edge cases, or parameter relationships. Given no output schema and minimal annotation coverage, the description is adequate but not rich.
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 only mentions voice_ids and sample_ids for bulk support. The description doesn't clarify the meaning of voice_id, sample_id, or account parameters beyond what the schema property names imply. It adds minimal value for a 5-parameter tool with zero schema documentation.
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 clearly states 'Retrieve the visual waveform of a voice sample' with a specific verb (retrieve) and resource (voice sample visual waveform). It distinguishes from siblings like elevenlabs_get_pvc_sample_audio and elevenlabs_get_pvc_sample_speakers by specifying 'visual waveform'.
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 context by describing the resource (voice sample visual waveform) and mentions bulk support for batched execution with voice_ids and sample_ids. However, it doesn't explicitly contrast with alternatives like elevenlabs_get_pvc_sample_audio or provide when-not-to-use guidance.
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.