List Dubbing Language Targets. List a project's language targets (cursor-paginated).
Bulk support: accepts project_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| cursor | No | ||
| status | No | ||
| account | No | ||
| page_size | No | ||
| project_id | Yes | ||
| project_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 already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering safety. The description adds behavioral details beyond annotations: cursor-paginated results and bulk support via project_ids. This transparently discloses pagination and batching behavior, though it does not describe response structure or rate limits.
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 extremely concise: two sentences that immediately convey the primary purpose, pagination, and bulk capability. No fluff or redundant filler; it is appropriately sized for a list operation.
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?
While the description covers the core purpose and bulk/pagination, it lacks details about response contents (no output schema) and does not clarify optional parameters like status or account. Given the tool's moderate complexity (6 params, no schema descriptions), more context would be beneficial, but it is not entirely deficient.
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 only explicitly mentions project_ids for bulk and implicitly references pagination via 'cursor-paginated'. Other parameters (cursor, status, account, page_size, project_id) are unexplained, leaving the agent to infer their purpose without guidance.
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 operation: 'List Dubbing Language Targets' and refines it to 'List a project's language targets (cursor-paginated)'. It distinguishes from sibling tools like elevenlabs_dubbing_language_get (which presumably retrieves a single target) by emphasizing the list and pagination behavior.
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 'Bulk support: accepts project_ids for batched execution', indicating when to use this tool for multiple projects, but it does not explicitly state when to use it over alternatives like twelve_labs_dubbing_language_get or create/delete. No explicit exclusions or alternative tool references are provided.
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.