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@aiwerk/mcp-server-elevenlabs

@aiwerk/mcp-server-elevenlabs

by AIWerk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

set_rules

Add or update alias and phoneme rules on a pronunciation dictionary by ID to control exact word and phrase pronunciation.

Instructions

Set Rules On The Pronunciation Dictionary

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "pronunciation_dictionary_id": "<pronunciation_dictionary_id>",
      "rules": "<rules>"
    },
    "name": "set_rules"
  }
}

Input 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

C2.8/5.0
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 the word 'Set'; it does not explain replacement semantics, how existing rules are affected, or error behavior.

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 description is a single short, front-loaded phrase with no wasted words, which is structurally clean. However, it is under-specified for a mutation tool and reads more like a title than a complete instruction.

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 mutation tool that sets rules on a pronunciation dictionary, the description omits crucial context: whether existing rules are replaced or merged, how duplicates are handled, and what happens to rules not included in the request. With no output schema and sibling add/remove tools, the agent lacks enough detail to call this confidently.

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%, so the schema already documents both required parameters, including the detailed rule object structure. The description adds no additional meaning or syntax for pronunciation_dictionary_id or rules, so baseline 3 is appropriate.

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

Purpose4/5

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

The description states a clear verb ('Set') and resource ('Rules On The Pronunciation Dictionary'), so the agent knows this acts on pronunciation dictionary rules. However, it does not distinguish the operation from sibling tools like add_rules or remove_rules, leaving the exact scope ambiguous.

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?

The description provides no when-to-use guidance, prerequisites, or alternatives. It does not mention when to use set_rules versus add_rules or remove_rules, leaving the agent to infer selection 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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