FFmpeg MCP Server
MCP-сервер FFmpeg
Этот файл README содержит информацию о репозитории ffmpeg-mcp , который содержит сервер MCP, предназначенный для работы с FFmpeg. Ниже вы найдете подробную информацию о репозитории, а также ссылку для доступа к последним выпускам.
🌟 О репозитории
Имя репозитория: ffmpeg-mcp
Краткое описание: MCP-сервер для FFmpeg.
Темы: Не предоставлено
Related MCP server: Project Content Server
🚀 Начать
Чтобы получить доступ к последним версиям сервера MCP для FFmpeg, нажмите на ссылку ниже:
Если ссылка содержит часть пути, загрузите указанный файл и запустите его, чтобы начать использовать сервер MCP.
Если ссылка включает только домен, просто перейдите по предоставленной ссылке, чтобы изучить и загрузить последние версии.
Если предоставленная ссылка не работает или была предоставлена не вами, мы рекомендуем проверить раздел «Релизы» этого репозитория на предмет последних обновлений.
📦 Особенности
Вот некоторые ключевые особенности репозитория ffmpeg-mcp :
Совместимость с MCP Server: создан специально для бесперебойной работы с FFmpeg.
Повышенная производительность: оптимизирована для повышения эффективности и скорости выполнения задач по обработке мультимедиа.
Безопасная связь: реализует безопасные протоколы связи для передачи данных.
Масштабируемая архитектура: разработана для обработки больших объемов запросов на обработку мультимедиа.
🛠️ Установка
Чтобы установить сервер MCP для FFmpeg, выполните следующие действия:
Загрузите последнюю версию по предоставленной ссылке.
Запустите загруженный файл, чтобы начать процесс установки.
Для завершения установки следуйте инструкциям на экране.
Начните использовать сервер MCP с FFmpeg для обработки медиаданных.
📜 Использование
После установки вы можете использовать сервер MCP с FFmpeg, выполнив следующие действия:
Настройте параметры сервера в соответствии с вашими требованиями.
Подключите сервер к вашей среде FFmpeg.
Начните отправлять задачи обработки мультимедиа на сервер MCP для выполнения.
Контролируйте ход работы и производительность сервера через предоставленный интерфейс.
📈 Дорожная карта
Будущие обновления для сервера MCP в этом репозитории могут включать:
Интеграция с дополнительными инструментами обработки медиаданных.
Расширенные функции безопасности для защиты данных.
Оптимизация производительности для более высокой скорости обработки.
Улучшенная масштабируемость для обработки больших рабочих нагрузок.
🤝 Вклад
Если вы заинтересованы в участии в разработке сервера MCP в этом репозитории, пожалуйста, следуйте этим рекомендациям:
Создайте форк репозитория и внесите необходимые изменения.
Отправьте запрос на внесение изменений с подробным описанием изменений.
Ваши вклады будут рассмотрены ответственными за репозиторий на предмет возможного включения.
📧 Контакты
Если у вас есть вопросы, отзывы или предложения относительно сервера MCP для FFmpeg в этом репозитории, свяжитесь с нами по адресу developer@example.com .
Следуя этому README, вы сможете быстро начать работу с репозиторием ffmpeg-mcp и использовать возможности сервера MCP для задач обработки медиа. Для получения более подробной информации изучите файлы, документацию и релизы репозитория. Спасибо за ваш интерес к нашему проекту! 🎉
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. Dates show when Glama detected each change.
2 tool updates
v1.0.0- First observed
extract_audio - First observed
speed_up
TDQS
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.
MCP server for Wan AI video generation
MCP server for Google Veo AI video generation
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