Get Metadata For A Pronunciation Dictionary. Get metadata for a pronunciation dictionary
Bulk support: accepts pronunciation_dictionary_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| 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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description's 'Get metadata' is consistent with those. The bulk-execution note is extra context beyond annotations, but the description does not disclose what metadata is returned or any operational constraints. No contradiction exists.
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 brief and front-loads the verb, but it wastes space by repeating the same idea: 'Get Metadata For A Pronunciation Dictionary' followed by 'Get metadata for a pronunciation dictionary.' The bulk-support line is useful, yet the duplication prevents a higher score.
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?
For a read-only tool with helpful annotations, the description covers the main action and batch capability adequately. However, there is no output schema and the description does not clarify return contents, the undocumented 'account' parameter, or differentiate from the plural sibling tool. It is serviceable but leaves gaps.
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 carry parameter meaning. It explains 'pronunciation_dictionary_ids' for batched execution and implies the singular ID, but it never describes the 'account' parameter or the relationship between the required singular ID and the bulk array. This is only partial compensation for a low-coverage schema.
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 uses a specific verb ('Get') and identifies the resource ('metadata for a pronunciation dictionary'), making the core purpose clear. It also adds a bulk-execution detail via 'pronunciation_dictionary_ids'. However, it does not explicitly distinguish this from the closely named sibling elevenlabs_get_pronunciation_dictionaries_metadata.
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 bulk-support statement gives some usage guidance: pass pronunciation_dictionary_ids for batched execution. But the description does not state when to prefer this tool over the plural sibling, nor does it mention prerequisites or exclusions. Usage context is implied rather than explicit.
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.