FFmpeg MCP Server
FFmpeg MCP 서버
이 README는 FFmpeg와 호환되도록 설계된 MCP 서버가 포함된 ffmpeg-mcp 저장소에 대한 정보를 제공합니다. 아래에서 저장소에 대한 자세한 정보와 최신 릴리스에 액세스할 수 있는 링크를 확인할 수 있습니다.
🌟 저장소 정보
저장소 이름: ffmpeg-mcp
간략한 설명: FFmpeg용 MCP 서버
주제: 제공되지 않음
Related MCP server: Project Content Server
🚀 시작하기
FFmpeg용 MCP 서버의 최신 릴리스에 액세스하려면 아래 링크를 클릭하세요.
링크에 경로 부분이 포함되어 있는 경우 지정된 파일을 다운로드하여 실행하여 MCP 서버를 사용하세요.
링크에 도메인만 포함된 경우 제공된 링크를 방문하여 최신 릴리스를 탐색하고 다운로드하세요.
제공된 링크가 작동하지 않거나 사용자가 제공하지 않은 경우, 이 저장소의 "릴리스" 섹션에서 최신 업데이트를 확인하는 것이 좋습니다.
📦 특징
ffmpeg-mcp 저장소의 주요 기능은 다음과 같습니다.
MCP 서버 호환성: FFmpeg와 원활하게 작동하도록 특별히 제작되었습니다.
향상된 성능: 미디어 처리 작업의 효율성과 속도를 최적화했습니다.
안전한 통신: 데이터 전송을 위한 안전한 통신 프로토콜을 구현합니다.
확장 가능한 아키텍처: 대량의 미디어 처리 요청을 처리하도록 설계되었습니다.
🛠️ 설치
FFmpeg용 MCP 서버를 설치하려면 다음 단계를 따르세요.
제공된 링크에서 최신 릴리스를 다운로드하세요.
다운로드한 파일을 실행하여 설치 과정을 시작하세요.
화면의 지시에 따라 설치를 완료하세요.
미디어 처리가 필요할 때 FFmpeg와 함께 MCP 서버를 사용해보세요.
📜 사용법
설치가 완료되면 다음 단계에 따라 FFmpeg와 함께 MCP 서버를 사용할 수 있습니다.
요구 사항에 맞게 서버 설정을 구성하세요.
서버를 FFmpeg 환경에 연결합니다.
미디어 처리 작업을 MCP 서버로 전송하여 실행합니다.
제공된 인터페이스를 통해 서버의 진행 상황과 성능을 모니터링합니다.
📈 로드맵
이 저장소의 MCP 서버에 대한 향후 업데이트에는 다음이 포함될 수 있습니다.
추가 미디어 처리 도구와의 통합.
데이터 보호를 위한 보안 기능이 강화되었습니다.
더 빠른 처리 속도를 위한 성능 최적화.
더 큰 작업 부하를 처리할 수 있는 확장성이 향상되었습니다.
🤝 기여하기
이 저장소에서 MCP 서버 개발에 기여하고 싶다면 다음 지침을 따르세요.
저장소를 포크하여 원하는 변경 사항을 적용합니다.
수정 사항에 대한 자세한 설명과 함께 풀 리퀘스트를 제출하세요.
귀하의 기여는 저장소 관리자가 잠재적으로 포함될지 여부를 검토합니다.
📧 문의하기
이 저장소에 있는 FFmpeg용 MCP 서버와 관련하여 질문, 피드백 또는 제안 사항이 있으시면 developer@example.com 으로 연락해 주시기 바랍니다.
이 README를 따라 ffmpeg-mcp 저장소를 빠르게 시작하고 미디어 처리 작업에 MCP 서버의 기능을 활용할 수 있습니다. 더 자세한 정보는 저장소의 파일, 문서 및 릴리스를 참조하세요. 저희 프로젝트에 관심을 가져주셔서 감사합니다! 🎉
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.
MCP server for Wan AI video generation
MCP server for Google Veo AI video generation
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