Update Dubbing Transcript Segment. Enterprise only. Edit a source segment's text, speaker, or timing.
Bulk support: accepts project_ids, segment_ids, speaker_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| text | No | ||
| end_s | No | ||
| account | No | ||
| start_s | No | ||
| project_id | Yes | ||
| segment_id | Yes | ||
| speaker_id | No | ||
| project_ids | No | ||
| segment_ids | No | ||
| speaker_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?
The description aligns with annotations by framing the operation as a mutation ('Update', 'Edit'). It adds useful behavioral context such as the enterprise-only restriction and batched execution support. However, it does not disclose what happens to omitted fields, the meaning of null values, or whether bulk updates are atomic or partial.
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 short and front-loaded: the core action is stated first, then the enterprise restriction, then editable fields, then bulk support. The first sentence is somewhat redundant with the tool name but the overall text is efficient and free of unnecessary detail.
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 10 parameters, no output schema, and no parameter-level schema documentation, the description is too thin. It omits important context such as expected return values, partial-update behavior, null-field handling, and how the bulk parameters interact with the required singular parameters.
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 coverage is 0%, so the description must compensate. It identifies the editable fields (text, speaker, timing) and mentions bulk array parameters, but leaves several parameters unexplained, including 'account', the relationship between singular and plural parameters, and null-value semantics. For 10 parameters, this is insufficient.
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 updates a dubbing transcript segment and specifies the editable aspects: text, speaker, or timing. The phrase 'source segment' helps differentiate from target-transcript segment siblings, though it does not explicitly identify the closely named 'elevenlabs_dubbing_transcript_segments_update' sibling.
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 some usage context: it is enterprise-only, edits source segments, and supports bulk execution via arrays. However, it does not explain when to use this tool versus the plural 'segments_update' sibling or the target-transcript segment tools, leaving the agent without explicit alternative-selection 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.