Update Pvc Voice Sample. Update a PVC voice sample - apply noise removal, select speaker, change trim times or file name.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| voice_id | Yes | ||
| file_name | No | ||
| sample_id | Yes | ||
| trim_end_time | No | ||
| trim_start_time | No | ||
| selected_speaker_ids | No | ||
| remove_background_noise | 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?
The description matches the annotations: readOnlyHint=false and destructiveHint=false, so it is consistent. It adds some behavioral context by specifying what can be changed, but it does not mention side effects, reversibility, permission requirements, or what happens to the sample after editing.
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, but the first sentence largely repeats the second. The redundancy is minor because the second sentence quickly adds the specific update dimensions.
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 0% schema description coverage, this description is thin. It names the operation and some editable fields but does not explain the required voice_id/sample_id relationship, trim time format, how speaker IDs are selected, or any post-edit implications.
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%, so the description must compensate. It gives plain-language meaning for several parameters: noise removal, speaker selection, trim times, and file name. However, it does not clarify trim time units, required identifiers, the account parameter, or the semantics of selected_speaker_ids beyond 'select speaker'.
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 uses a specific verb and resource: 'Update a PVC voice sample' and names the supported modifications (noise removal, speaker selection, trim times, file name). This clearly distinguishes it from sibling tools like create_pvc_voice, delete_pvc_voice_sample, and get_pvc_sample_audio.
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 usage context is implied by 'Update' and the list of editable attributes, but there is no explicit guidance about when to choose this over alternatives, prerequisites, or cases where it should not be used.
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.