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elevenlabs-sound-effect-server

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elevenlabs-sound-effect-server MCP Server

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A Model Context Protocol server for generating sound effects using ElevenLabs API

This is a TypeScript-based MCP server that implements sound effect generation functionality. It provides:

  • A tool for generating sound effects from text descriptions

  • Automatic saving of generated sound effects as MP3 files

Features

Tools

  • generate_sound_effect - Generate sound effects using ElevenLabs API

    • Takes text description as required parameter

    • Generates MP3 sound effect file based on the description

    • Saves generated files in the sounds directory

Related MCP server: AudioGen MCP Server

Development

Install dependencies:

npm install

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

Installation

Environment Variables

Before running the server, you need to set up your ElevenLabs API key:

export ELEVENLABS_API_KEY=your_api_key_here

Configuration

To use with Claude Desktop, add the server config:

On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "elevenlabs-sound-effect-server": {
      "command": "/path/to/elevenlabs-sound-effect-server/build/index.js"
    }
  }
}

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

Available Tools

1 tool
generate_sound_effectB

Generate sound effect using ElevenLabs API

ParametersJSON Schema
NameRequiredDescriptionDefault
textYesText description of the sound effect to generate

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations provided, the description bears full responsibility for disclosing behavioral traits. It only mentions using an external API, without detailing side effects (e.g., cost, latency, failure modes) or safety characteristics. Important behaviors are missing.

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 a single sentence that conveys the essential information with no waste. Every word serves a purpose, and it is appropriately front-loaded.

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?

The description fails to mention what the tool returns (e.g., audio file URL, binary data) or any usage constraints. Given the absence of an output schema, the description should compensate by clarifying the output format, but it does not.

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%, and the single parameter 'text' already has a clear description in the schema. The tool description adds marginal value by confirming the context ('using ElevenLabs API'), so a baseline score of 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 clearly states the action ('generate') and resource ('sound effect') and even specifies the API source ('ElevenLabs API'). It is straightforward and easy to understand, though it does not differentiate from any sibling tools (none exist).

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?

No explicit guidance on when to use this tool or alternatives is provided. Since no sibling tools exist, the usage context is implicitly clear, but the description lacks any 'when-not-to-use' or prerequisite information.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.1.0
    • First observedgenerate_sound_effect
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.3/5.0

Scored across 1 tool

Disambiguation5/5

Only one tool exists, so there is no possibility of confusion between tools.

Naming Consistency5/5

With a single tool, naming is inherently consistent; the verb_noun pattern is appropriate.

Tool Count2/5

One tool is too few for the apparent scope of a sound effect server; likely missing related operations like listing voices or effects.

Completeness2/5

The tool surface is severely incomplete; only generation is provided, with no support for other essential operations (e.g., listing, deletion) for a full-service API.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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