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AIWerk

@aiwerk/mcp-server-elevenlabs

by AIWerk

add_from_rules

Add a pronunciation dictionary using alias or phoneme rules to control how words are pronounced in ElevenLabs text-to-speech.

Instructions

Add A Pronunciation Dictionary

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name of the pronunciation dictionary, used for identification only.
rulesYesList of pronunciation rules. Rule can be either: an alias rule: {'string_to_replace': 'a', 'type': 'alias', 'alias': 'b', } or a phoneme rule: {'string_to_replace': 'a', 'type': 'phoneme', 'phoneme': 'b', 'alphabet': 'ipa' }
descriptionNo
workspace_accessNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, and destructiveHint=false, so the safety profile is covered. The description adds no behavioral context beyond that, not explaining side effects, permissions, or how the created dictionary behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The single sentence is short and not bloated, but it is under-specified for a four-parameter creation tool. It does not front-load the distinguishing detail that the dictionary is created from a rules list.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool that accepts a structured array of pronunciation rules plus optional metadata and workspace access, the description is far too thin. It omits the rules-based nature of the operation, optional parameters, and any indication of the creation result or output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 50%: name and rules are documented in the schema, but description and workspace_access are not. The description itself adds no meaning for any parameter, so it fails to compensate for the undocumented optional fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Add A Pronunciation Dictionary'), but it does not mention that the dictionary is built from rules, despite the tool name add_from_rules. It also fails to distinguish the tool from siblings like add_rules, add_from_file, or set_rules.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance about when to use this tool versus alternatives. The description gives no prerequisites, context, or exclusions, leaving the agent to infer usage from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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