ofaudio-mcp

add_pronunciation_rules

Add pronunciation rules to an existing dictionary to control word pronunciation in text-to-speech, using a dictionary name or ID.

Instructions

Add rules to an existing pronunciation dictionary (by saved name or id).

Args: dictionary: A saved dictionary name (or an ElevenLabs dictionary id). rules: {word: say_as} map or a list of EL rule objects (see create_pronunciation_dictionary).

Returns: {"id", "version_id", "added": } or {"error"}.

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": {
      "dictionary": "<dictionary>",
      "rules": "<rules>"
    },
    "name": "add_pronunciation_rules"
  }
}

Input Schema

Name	Required	Description	Default
`rules`	Yes
`dictionary`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 31, 2026

TDQS

B3.3/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden. It states it adds rules (implying mutation) and shows a return shape, but it does not disclose permissions, reversibility, idempotency, or any side effects like versioning. For a mutation tool, this is a significant transparency gap.

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

Conciseness5/5

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

The description is compact and well-structured with Args and Returns sections. Every sentence contributes meaning; there is no fluff. It is easy to scan and understand.

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

Completeness3/5

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

The description covers the core operation and parameters, and provides a return format. However, with no annotations and no output schema, it lacks broader context such as when to choose this tool, error scenarios beyond a generic error object, and any prerequisite knowledge besides referencing another tool. It is adequate but not fully complete.

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?

The schema has no descriptions, so the description compensates by explaining 'dictionary' as a saved name or id and 'rules' as a map or list of EL rule objects. It adds meaning beyond the bare property names, though it defers detailed rule structure to another tool, which is a partial gap.

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 clearly states the tool adds rules to an existing pronunciation dictionary, specifying that the dictionary can be referenced by saved name or id. This is a specific verb+resource pairing that makes the main function clear, though it does not explicitly contrast with sibling tools like create_pronunciation_dictionary.

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

Usage Guidelines3/5

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

The description implies usage (you'd use this when you want to add rules to an existing dictionary) but does not provide explicit when-to-use or when-not-to-use guidance. It does reference create_pronunciation_dictionary for rule object format, which is a cross-reference, but lacks alternatives or exclusions.

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