Cursor Sound MCP
Sonido del cursor MCP
Una implementación del Protocolo de Contexto de Modelo (MCP) que reproduce efectos de sonido después de que Cursor AI complete la generación de código. Este MCP se integra con Cursor para proporcionar retroalimentación de audio y ofrecer una experiencia de codificación más interactiva.
Inspirado por @EricListin en X.com: hice algunos cambios en el código para no generar errores y estar en línea.
Características
Reproduce un efecto de sonido cuando el cursor completa la generación de código
Utiliza el Protocolo de Contexto de Modelo (MCP) para la integración estandarizada
Efectos de sonido configurables
Manejo y registro de errores mejorados
Formato de respuesta JSON estable
Related MCP server: MCP Sound Tool
Reglas del cursor
Agregue esto a sus instrucciones personalizadas del cursor:
"CADA VEZ que termines alguna tarea o necesites algo de mí, ejecuta el servidor MCP sound-mcp para llamar mi atención".
Instalación
Instalar dependencias:
npm installAñade tus efectos de sonido: Coloca tus archivos de sonido en el directorio
sounds. El sonido predeterminado esperado es:
sounds/completion.mp3- Se reproduce después de la generación del código
Puedes encontrar efectos de sonido gratuitos en freesound.org.
Construir el proyecto:
npm run buildUso
Ejecute el servidor MCP:
npm startEl servidor se iniciará y escuchará eventos del Cursor a través del transporte stdio.
Configuración
Los efectos de sonido y el volumen se pueden personalizar mediante:
Reemplazo de los archivos de sonido en el directorio
soundsModificar las rutas de los archivos de sonido en
src/index.ts
Desarrollo
Para el desarrollo con recompilación automática:
npm run devAvailable Tools
1 toolplayCompletionSoundC
Plays a completion sound when called
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool plays a sound, implying an auditory output, but doesn't disclose behavioral traits like whether it requires audio permissions, if it blocks execution, error handling, or what happens on failure. This is a significant gap for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's appropriately sized for a simple tool and front-loaded with the core action. Every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema), the description is incomplete. It lacks context about the sound's source, volume, duration, or system requirements. With no annotations to fill gaps, the description should provide more detail to ensure the agent understands how to invoke it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100% (as there are no parameters to describe). The description doesn't need to add parameter semantics, so a baseline score of 4 is appropriate, reflecting that no additional information is required beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose ('Plays a completion sound when called'), which is clear but vague. It specifies the action ('Plays') and resource ('a completion sound'), but lacks detail about what constitutes a 'completion sound' or how it's played. With no sibling tools, differentiation isn't needed, but the purpose remains somewhat ambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool. It doesn't specify appropriate contexts (e.g., after a task finishes), prerequisites, or alternatives. With no sibling tools, this isn't a comparative issue, but the lack of any usage context leaves the agent without direction.
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 tool update
v1.0.0- First observed
playCompletionSound
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The single tool has a clear, distinct purpose that cannot be mistaken for another.
A single tool inherently has perfect naming consistency, as there are no other names to compare it against. The tool name 'playCompletionSound' follows a clear verb_noun pattern.
One tool is too few for a server's purpose, as it severely limits functionality and suggests the server is trivial or incomplete. A single sound-playing tool does not justify a full MCP server.
The server is severely incomplete, as it only offers a single sound-playing tool with no other related operations (e.g., stop sound, list sounds, adjust volume). This minimal surface fails to cover any meaningful domain.
Maintenance
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Related MCP Servers
- FlicenseDqualityDmaintenanceProvides audio feedback by playing sound effects when Cursor AI completes code generation, creating a more interactive coding experience.119-
- AlicenseAqualityFmaintenanceA Model Context Protocol implementation that plays sound effects (completion, error, notification) for Cursor AI and other MCP-compatible environments, providing audio feedback for a more interactive coding experience.31MIT
- AlicenseNot gradedqualityFmaintenanceProvides audio playback functionality for AI agents, allowing them to play notification sounds when coding tasks are completed.1MIT
- AlicenseBqualityDmaintenancePlays sound effects (completion, newtype, and error sounds) in response to various situations like task completion, insights, or errors. Integrates with Claude Desktop to provide audio feedback for improved workflow efficiency and entertainment.216 npm6MIT