Set Rules On The Pronunciation Dictionary. Replaces all existing rules on the pronunciation dictionary with the provided ones.
Bulk support: accepts pronunciation_dictionary_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| rules | Yes | ||
| account | 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 discloses a destructive overwrite behavior: 'Replaces all existing rules on the pronunciation dictionary with the provided ones.' This contradicts the annotation destructiveHint: false, which indicates the operation is not destructive. This is a significant inconsistency.
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 sentences with no filler. It front-loads the core purpose, then states the critical replacement behavior and bulk-support capability. Every sentence earns its place.
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 4 parameters, no output schema, and weak annotations, so the description carries a heavy burden. It covers the high-level operation and bulk support, but does not explain required parameter semantics, expected return value, prerequisites, or error scenarios. The annotation contradiction further undermines completeness.
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 adds meaning for the bulk parameter by noting 'accepts pronunciation_dictionary_ids for batched execution.' The required 'rules' and 'pronunciation_dictionary_id' parameters remain undocumented, and the complex rules structure is not explained beyond the schema's anyOf definitions.
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's function: 'Set Rules On The Pronunciation Dictionary' and 'Replaces all existing rules ... with the provided ones.' This distinguishes it from sibling tools like elevenlabs_add_rules and elevenlabs_remove_rules by emphasizing full replacement rather than incremental modification.
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 this tool by explicitly noting it replaces all existing rules, which contrasts with additive alternatives such as elevenlabs_add_rules. It also mentions bulk support via pronunciation_dictionary_ids. However, it does not explicitly name alternative tools or state when not to use it.
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.