Update Dubbing Target Transcript Segments. Enterprise only. Edit several segments' translations for a language target in one atomic request.
Bulk support: accepts project_ids, language_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| segments | Yes | ||
| project_id | Yes | ||
| language_id | Yes | ||
| project_ids | No | ||
| language_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 minimal (readOnlyHint false, idempotentHint false, destructiveHint false). The description adds the behavior of an 'atomic request' and 'bulk support', which go beyond the annotations. However, it does not disclose whether edits overwrite or merge, what happens on partial failure, or other side effects. Given the sparse annotations, this is adequate but not rich.
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 concise and front-loaded with the primary purpose. It uses three short paragraphs: title, enterprise constraint, and bulk support note. No redundant or filler sentences. Slightly could be more structured but it is efficient.
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 tool has 6 parameters with 0% schema description coverage, no output schema, and nested objects, the description should provide more context. It covers atomicity and bulk support but omits details about parameter formats, how the segments map is keyed, usage of 'account', and error handling. This is a complex mutation tool, and the description is insufficient for a smooth agent invocation.
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 mentions 'segments' are for translations and that project_ids/language_ids enable batch execution, which adds some meaning. However, it does not explain the structure of the 'segments' object (which is an arbitrary object), the purpose of 'account', or the relationship between single and bulk parameters (project_id vs project_ids, language_id vs language_ids). The description partially bridges the gap but leaves key ambiguities.
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 action ('Update Dubbing Target Transcript Segments') and the resource (target transcript segments for dubbing). It also specifies the operation is about editing translations for a language target. However, it does not explicitly distinguish from sibling tools like elevenlabs_dubbing_transcript_segments_update or the other target transcript segment tool, so it stops short of a 5.
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 mentions 'Enterprise only' as a usage constraint and notes bulk support via project_ids/language_ids, but it does not explain when to use this tool versus the related segment update tools, nor does it provide any exclusions or alternative suggestions. It gives some context of when it is applicable (enterprise) but lacks explicit 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.