elevenlabs-mcp

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edit_voice_settings

Modify voice parameters such as stability, similarity, style, speed, or speaker boost for a specific voice ID. Only provided fields are changed.

Instructions

Update the settings for a voice (only provided fields are changed).

Args: voice_id: the voice id. stability / similarity_boost / style / speed: floats (0..1; speed ~0.7..1.2). use_speaker_boost: bool.

Returns the updated settings as JSON.

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": {
      "voice_id": "<voice_id>"
    },
    "name": "edit_voice_settings"
  }
}

Input Schema

Name	Required	Description	Default
`speed`	No
`style`	No
`voice_id`	Yes
`stability`	No
`similarity_boost`	No
`use_speaker_boost`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 8, 2026

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that only provided fields change, parameter ranges (e.g., speed 0.7–1.2), and return type (JSON). Minor omissions: no mention of idempotency or effect on existing streams.

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 concise: a single sentence for purpose, then a clear list of parameters and return type. No redundancy or filler. Front-loads the key behaviour.

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

Completeness4/5

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

Given the simple mutation nature and presence of output schema (returns JSON), the description covers purpose, partial update, parameter constraints, and return format. It lacks context on error handling or default behavior for omitted fields, but overall adequate.

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

Parameters5/5

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

Input schema has 0% description coverage, so the description compensates fully. It lists each parameter with type and constraints: voice_id as ID, stability et al. as floats with ranges, use_speaker_boost as bool. This adds meaningful semantics beyond the schema.

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

Purpose5/5

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

The description clearly states the tool updates voice settings and specifies that only provided fields are changed. This distinguishes it from sibling tools like get_default_voice_settings (read-only) and clone_voice (creation).

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 explains the partial update behavior but does not explicitly state when to use this tool versus alternatives (e.g., get_default_voice_settings for reading, or design_voice for creative generation). There is no mention of prerequisites or context for exclusion.

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