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AIWerk

@aiwerk/mcp-server-elevenlabs

by AIWerk

add_rules

Add alias or phoneme pronunciation rules to an existing ElevenLabs dictionary so specific words and phrases are spoken correctly by the model.

Instructions

Add Rules To The Pronunciation Dictionary

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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' }
pronunciation_dictionary_idYesThe id of the pronunciation dictionary

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.6/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, destructiveHint=false, and openWorldHint=true, so the mutation and non-idempotency profile is covered structurally. The description adds nothing beyond that — no mention of whether rules are appended, deduplicated, or validated, nor any auth/permission context.

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

Conciseness4/5

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

A single short sentence with zero padding, front-loaded with the action. It is efficient, though the brevity is partly under-specification rather than disciplined conciseness.

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 non-idempotent mutation on a nested rules array with no output schema, the description should say what happens on repeated calls, whether existing rules are preserved, and what a successful result looks like. None of that is present, leaving real gaps for an agent.

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

Parameters3/5

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

Schema description coverage is 100%, including a detailed breakdown of alias vs phoneme rule shapes, so the schema already carries the parameter burden. The description adds no syntax, format, or constraint detail beyond that, which is the baseline 3 case.

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 verb (add) and resource (rules to the pronunciation dictionary), but it essentially restates the tool name/title with no differentiating detail. With close siblings like set_rules, remove_rules, and add_from_rules present, an agent cannot tell from this text alone which one to pick.

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 on when to use this tool versus set_rules (which likely replaces rules), remove_rules, or add_from_rules. No prerequisites, no context about dictionary state, no indication of whether adding is additive or overwriting.

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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