Update Dubbing Target Transcript Segment. Enterprise only. Edit a segment's translation for a language target.
Bulk support: accepts project_ids, language_ids, segment_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| project_id | Yes | ||
| segment_id | Yes | ||
| language_id | Yes | ||
| project_ids | No | ||
| segment_ids | No | ||
| translation | 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 exist but all flags are false, providing minimal safety context. The description adds useful behavioral details beyond annotations: 'Enterprise only' indicates an access restriction, and 'Bulk support' reveals batched execution behavior. It does not disclose whether updates overwrite existing translations or any side effects of editing, but the added context is valuable.
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 two compact sentences. The first sentence front-loads the purpose and constraint ('Enterprise only'), the second adds the bulk execution detail. Every word earns its place with no redundancy or fluff.
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?
The tool has 8 parameters, no output schema, and complex bulk/single modes. The description does not explain parameter relationships, response format, error conditions, or how bulk mode interacts with required singular parameters. It also fails to differentiate from a near-identically named sibling, leaving critical gaps for correct 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 names plural parameters (project_ids, language_ids, segment_ids) for bulk support but does not explain the singular required fields (project_id, language_id, segment_id), the 'translation' parameter's meaning, or the relationship between singular and plural forms. This leaves significant ambiguity for agents.
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 verb 'Update' and the resource 'Dubbing Target Transcript Segment', and elaborates with 'Edit a segment's translation for a language target.' This specifies the action and object. However, it does not differentiate between the similarly named sibling tool 'elevenlabs_dubbing_target_transcript_segmen_fa79db', so it lacks explicit sibling differentiation.
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 through 'Edit a segment's translation for a language target' and the 'Enterprise only' constraint. It mentions bulk support with plural arrays, suggesting when to use bulk execution. However, it offers no explicit guidance on when to use this tool versus alternatives like elevenlabs_dubbing_transcript_segment_update or the other target transcript segment tool.
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.