Update Pronunciation Dictionary. Partially update the pronunciation dictionary without changing the version
Bulk support: accepts pronunciation_dictionary_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | ||
| account | No | ||
| archived | No | ||
| pronunciation_dictionary_id | Yes | ||
| pronunciation_dictionary_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 adds useful behavioral context beyond the annotations: the update is partial and does not change the version, and multiple IDs can be processed in batch. It does not disclose response behavior, error cases, or side effects, but the provided notes are relevant and non-contradictory.
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 compact and front-loaded with the core purpose, followed by a short bulk-support note. The slight redundancy between 'Update' and 'Partially update' is minor and does not meaningfully hurt clarity.
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 no output schema, five parameters, and no explanation of return behavior or parameter semantics, the description is only marginally sufficient. It gives a high-level action and batch nuance but leaves significant gaps for an agent to invoke the tool correctly.
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%, and the description only explains the role of pronunciation_dictionary_ids for batch execution. It does not clarify the semantics of 'name', 'account', 'archived', or the singular 'pronunciation_dictionary_id'. This leaves most parameters underspecified.
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 identifies the tool as updating a pronunciation dictionary and adds the key detail that it performs a partial update without changing the version. It does not explicitly differentiate itself from the sibling tool 'elevenlabs_update_pronunciation_dictionaries', but the patch semantics are evident from both the name and description.
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 when to use the tool: for partial updates that preserve the dictionary version. It also explicitly calls out bulk support via pronunciation_dictionary_ids, which is useful. However, it does not state when to prefer this over the sibling update tool or mention exclusions/alternatives.
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.