Start Speaker Separation. Start speaker separation process for a 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 are all false (readOnlyHint=false, destructiveHint=false, idempotentHint=false), so the description must disclose side effects. It only says 'Start speaker separation process,' implying an asynchronous or long-running operation, but does not explain what happens after starting, whether it can be run multiple times, costs, or if it returns a job ID. This is insufficient for a mutating action.
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 very short (two sentences) but the first sentence redundantly repeats the tool name and the second sentence adds bulk support info. It is front-loaded and gets to the point, but the repetition could be trimmed. No verbose content, but not optimally structured.
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?
For a tool that starts a process with no output schema, no useful annotations, and zero schema descriptions, the description is too sparse. It does not mention whether a job ID is returned, how to check progress, or any follow-up tools. The bulk support note is useful but does not compensate for missing behavioral and outcome details.
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 explicitly mentions bulk support with voice_ids and sample_ids, clarifying that these arrays are for batch execution. However, it does not explain the purpose of 'account' or the relationship between required single params (voice_id, sample_id) and the bulk arrays. Provides some value but leaves gaps.
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 the tool starts a speaker separation process on a sample, using the verb 'start' and specifying the resource. It is distinct from sibling tools (e.g., get_speaker_audio, get_pvc_sample_speakers) though not explicitly named, but the purpose is unambiguous. The redundancy between the title and first sentence slightly detracts, but the meaning is clear.
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 when a speaker separation process needs to be initiated, and mentions bulk execution support, which gives context. However, it does not mention when not to use this tool, alternatives for retrieving results, or any prerequisites (e.g., sample must already exist). Minimal guidance beyond the basic scenario.
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.