Get Voices. Retrieves a list of shared voices.
Bulk support: accepts owner_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| age | No | ||
| page | No | ||
| sort | No | ||
| accent | No | ||
| gender | No | ||
| locale | No | ||
| search | No | ||
| account | No | ||
| category | No | ||
| featured | No | ||
| language | No | ||
| owner_id | No | ||
| owner_ids | No | ||
| page_size | No | ||
| use_cases | No | ||
| descriptives | No | ||
| reader_app_enabled | No | ||
| include_custom_rates | No | ||
| include_live_moderated | No | ||
| min_notice_period_days | 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, idempotentHint=true, and destructiveHint=false, covering safety. The description adds the bulk execution capability with owner_ids, which is beyond annotations, but does not explain pagination, response structure, or other behavioral details.
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 extremely concise, with two sentences that deliver the core purpose and a key usage note. No filler or redundant information.
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?
Given the large number of parameters and no output schema, the description is severely under-specified. It does not explain filtering options, return format, pagination, or any example usage, making it inadequate for an agent to select and invoke the tool effectively.
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?
With 20 parameters and 0% schema description coverage, the description must compensate for parameter semantics. It only mentions owner_ids for batch execution; all other parameters (age, accent, gender, etc.) are unexplained, leaving the agent without guidance on their meaning.
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 'Get Voices. Retrieves a list of shared voices.' It specifies the resource (shared voices) and the action (list), distinguishing it from other voice tools like elevenlabs_get_voices or user-specific voice tools.
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 provides context that this retrieves shared voices and notes bulk support via owner_ids, giving a clear usage context. However, it does not explicitly mention when not to use it or name 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.