FFmpeg MCP Server
Servidor MCP de FFmpeg
Este archivo README proporciona información sobre el repositorio ffmpeg-mcp , que contiene un servidor MCP diseñado para funcionar con FFmpeg. A continuación, encontrará detalles sobre el repositorio, junto con un enlace para acceder a las últimas versiones.
🌟 Acerca del Repositorio
Nombre del repositorio: ffmpeg-mcp
Descripción breve: Un servidor MCP para FFmpeg
Temas: No proporcionados
Related MCP server: Project Content Server
🚀 Empezar
Para acceder a las últimas versiones del servidor MCP para FFmpeg, haga clic en el siguiente enlace:
Si el enlace incluye una parte de ruta, descargue el archivo especificado y ejecútelo para comenzar a utilizar el servidor MCP.
Si el enlace solo incluye el dominio, simplemente visite el enlace proporcionado para explorar y descargar las últimas versiones.
Si el enlace proporcionado no funciona o no fue proporcionado por usted, le recomendamos consultar la sección "Lanzamientos" de este repositorio para obtener las últimas actualizaciones.
📦 Características
Estas son algunas características clave del repositorio ffmpeg-mcp :
Compatibilidad con servidor MCP: diseñado específicamente para funcionar sin problemas con FFmpeg.
Rendimiento mejorado: optimizado para la eficiencia y la velocidad en las tareas de procesamiento de medios.
Comunicación segura: implementa protocolos de comunicación segura para la transferencia de datos.
Arquitectura escalable: diseñada para manejar grandes volúmenes de solicitudes de procesamiento de medios.
🛠️ Instalación
Para instalar el servidor MCP para FFmpeg, siga estos pasos:
Descargue la última versión desde el enlace proporcionado.
Ejecute el archivo descargado para iniciar el proceso de instalación.
Siga las instrucciones en pantalla para completar la instalación.
Comience a utilizar el servidor MCP con FFmpeg para sus necesidades de procesamiento de medios.
📜 Uso
Una vez instalado, puedes utilizar el servidor MCP con FFmpeg siguiendo estos pasos:
Configure los ajustes del servidor según sus requisitos.
Conecte el servidor a su entorno FFmpeg.
Comience a enviar tareas de procesamiento de medios al servidor MCP para su ejecución.
Supervise el progreso y el rendimiento del servidor a través de la interfaz proporcionada.
📈 Hoja de ruta
Las futuras actualizaciones para el servidor MCP en este repositorio pueden incluir:
Integración con herramientas de procesamiento de medios adicionales.
Funciones de seguridad mejoradas para la protección de datos.
Optimizaciones de rendimiento para velocidades de procesamiento más rápidas.
Escalabilidad mejorada para gestionar cargas de trabajo más grandes.
🤝 Contribuyendo
Si está interesado en contribuir al desarrollo del servidor MCP en este repositorio, siga estas pautas:
Bifurca el repositorio y realiza los cambios que desees.
Envíe una solicitud de extracción con una descripción detallada de las modificaciones.
Los mantenedores del repositorio revisarán sus contribuciones para determinar su posible inclusión.
📧 Contacto
Si tiene alguna pregunta, comentario o sugerencia sobre el servidor MCP para FFmpeg en este repositorio, no dude en comunicarse con nosotros a developer@example.com .
Siguiendo este archivo README, podrá comenzar rápidamente a usar el repositorio ffmpeg-mcp y aprovechar las capacidades del servidor MCP para sus tareas de procesamiento multimedia. Para obtener información más detallada, explore los archivos, la documentación y las versiones del repositorio. ¡Gracias por su interés en nuestro proyecto! 🎉
Available Tools
2 toolsextract_audioB
Extract audio as mp3 from a video
| Name | Required | Description | Default |
|---|---|---|---|
| input_file | Yes | Path to input file | |
| output_file | No | Path to output file, output to the same directory if not specified |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the output format (mp3) but lacks details on behavioral traits like error handling, performance constraints, or whether the operation is destructive to the input file. This leaves significant gaps for an agent to understand the tool's behavior.
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 that clearly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to understand at a glance.
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 moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral aspects and usage context, leaving room for improvement in completeness.
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?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as format details or usage examples, resulting in a baseline score of 3.
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 clearly states the action ('extract audio') and the resource ('from a video'), specifying the output format as mp3. However, it doesn't differentiate from the sibling tool 'speed_up', which appears to be a different operation, so it doesn't fully distinguish from siblings.
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?
No guidance is provided on when to use this tool versus alternatives. The description only states what it does, with no context about prerequisites, limitations, or comparison to the sibling tool 'speed_up'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
speed_upC
Speed up a video
| Name | Required | Description | Default |
|---|---|---|---|
| input_file | Yes | Path to input file | |
| max_fps | No | Max FPS for the output file | |
| output_file | No | Path to output file, output to the same directory if not specified | |
| speed_factor | No | Speed factor for the output file, default to 2x sped up |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'speed up' implies a transformation operation, it doesn't disclose whether this is destructive to the original file, what permissions are needed, or any rate limits. The description lacks essential behavioral context for a mutation tool.
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 extremely concise at just three words, front-loading the core purpose without any wasted language. Every word earns its place in this minimal but clear statement.
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?
For a video processing tool with 4 parameters and no annotations or output schema, the description is insufficient. It doesn't explain what the tool returns, what happens to the original file, or provide any context about the processing operation beyond the basic purpose.
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?
Schema description coverage is 100%, so the schema already fully documents all four parameters. The description adds no additional parameter information beyond what's in the schema, meeting the baseline expectation but not providing extra value.
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 clearly states the action ('speed up') and resource ('a video'), making the purpose immediately understandable. However, it doesn't differentiate from the sibling tool 'extract_audio', which handles a different video processing function.
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?
No guidance is provided about when to use this tool versus alternatives. The description doesn't mention the sibling tool 'extract_audio' or any other video processing options, leaving the agent without context for tool selection.
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.
2 tool updates
v1.0.0- First observed
extract_audio - First observed
speed_up
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: one extracts audio from video, while the other modifies video playback speed. There is no overlap in functionality, making it impossible to confuse them.
Both tools follow a consistent verb_noun naming pattern (extract_audio, speed_up), using clear action verbs and descriptive nouns. The naming is uniform and predictable throughout the set.
With only two tools, this server is severely under-scoped for an FFmpeg server, which typically handles a wide range of media processing tasks like format conversion, filtering, and metadata editing. The count feels incomplete for the domain.
The tool surface is extremely incomplete for an FFmpeg server, lacking core operations such as video format conversion, resolution changes, filtering, metadata extraction, or basic CRUD-like media processing. Agents will face significant gaps in functionality.
Related MCP Connectors
MCP server for the FFmpeg Micro video transcoding API — create, monitor, download transcodes.
Hosted MCP tools for FFmpeg-style video and audio processing through FFMPEG API.
Guardrailed video editing MCP server: 200 FFmpeg tools, Video Receipts, Shorts repurposing.
MCP server for Wan AI video generation
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