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elevenlabs_delete_pvc_voice_sample

Delete Pvc Voice Sample. Delete a sample from a PVC voice.

Bulk support: accepts voice_ids, sample_ids for batched execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountNo
voice_idYes
sample_idYes
voice_idsNo
sample_idsNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.8/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description explicitly says 'Delete a sample,' implying a destructive operation, yet the annotation destructiveHint is set to false. This is a direct contradiction. Additionally, there is no disclosure about irreversibility, permission requirements, or effects on the voice, which is critical for a deletion tool. The contradiction alone drops this to a 1.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact, front-loaded with the core action, and includes a brief note on bulk support. There is no redundant information, and every sentence adds value despite the contradictions. It is appropriately sized for the tool's simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a deletion tool with no output schema and no helpful annotations (which are also contradictory), the description lacks crucial context: irreversible effects, prerequisites (e.g., ownership), or interaction with the voice's other samples. It also fails to clarify bulk execution semantics, such as whether both single and bulk parameters can be used together. This is insufficient for a potentially destructive operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

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 only mentions voice_ids and sample_ids for bulk support, which is helpful but does not clarify the relationship with the required voice_id and sample_id parameters, nor whether they are alternatives or co-required. The description adds minimal value beyond the schema for these array parameters and ignores the single-field parameters entirely.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it deletes a sample from a PVC voice, which is specific and distinguishes it from generic delete_sample tools. It mentions bulk support via voice_ids and sample_ids, adding capability clarity. However, it doesn't explicitly contrast with sibling tools like elevenlabs_delete_sample, so it's not fully differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions bulk support using arrays, which gives some guidance on when to use bulk vs single execution. However, it doesn't explain when to use this tool over alternatives like elevenlabs_delete_sample or what distinguishes PVC voices, nor does it provide exclusions. The context is thin.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.4/5.0
Disambiguation2/5

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 Consistency1/5

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.

Tool Count1/5

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.

Completeness3/5

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.