elevenlabs_add_from_rules
Add A Pronunciation Dictionary. Creates a new pronunciation dictionary from provided rules.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| rules | Yes | ||
| account | No | ||
| description | No | ||
| workspace_access | No |
Add A Pronunciation Dictionary. Creates a new pronunciation dictionary from provided rules.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| rules | Yes | ||
| account | No | ||
| description | No | ||
| workspace_access | 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 are all false, so no safety information is provided. The description only says 'creates' without disclosing side effects, idempotency, required permissions, or what happens to existing data. It doesn't mention that it's a write operation or any other behavioral traits, leaving the agent without crucial operational context.
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 brief (two sentences) but under-specified rather than concise. It lacks essential details about parameters, use cases, and behavior, making it too minimal to be helpful. Front-loading is irrelevant when almost no content exists.
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?
With 5 parameters, a complex nested rules array, no output schema, and sparse annotations, the description is entirely inadequate. It fails to explain the structure of rules, the purpose of the dictionary, or how this tool fits into the broader workflow. It provides no context for correct invocation.
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 mentions 'rules' without explaining the two possible rule types (alias vs phoneme), required fields, or the meaning of other parameters like name, account, description, and workspace_access. It adds negligible value beyond the schema structure itself.
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 states it creates a new pronunciation dictionary from provided rules, which is a clear verb+resource. However, it doesn't differentiate from sibling tools like elevenlabs_add_rules, elevenlabs_set_rules, or elevenlabs_update_pronunciation_dictionaries, which could also involve rules and dictionaries.
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?
No guidance is given on when to use this tool versus alternatives. It doesn't mention whether it's for initialization only, or how it relates to updating or patching existing dictionaries. The description implies it's for creating new dictionaries, but no explicit context or exclusions 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.