FFmpeg MCP Server
The FFmpeg MCP Server enables media processing tasks via FFmpeg integration. Key capabilities include:
Speed Up Videos: Adjust video speed with customizable speed factors and frame rate settings
Extract Audio: Extract audio from videos and save it as an MP3 file
Customizable Output Paths: Specify output file paths or default to the input file's directory
Secure and Scalable: Handles large volumes of media processing tasks securely
An MCP server designed to work with FFmpeg for media processing tasks, enabling enhanced performance, secure communication, and scalable architecture for handling media processing requests.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@FFmpeg MCP Serverconvert this video to MP4 format"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
FFmpeg MCP Server
This README provides information about the ffmpeg-mcp repository, which contains an MCP server designed to work with FFmpeg. Below, you will find details about the repository, along with a link to access the latest releases.
🌟 About the Repository
Repository Name: ffmpeg-mcp
Short Description: An MCP server for FFmpeg
Topics: Not provided
Related MCP server: Project Content Server
🚀 Get Started
To access the latest releases of the MCP server for FFmpeg, click on the link below:
If the link includes a path part, please download the specified file and execute it to begin using the MCP server.
If the link only includes the domain, simply visit the provided link to explore and download the latest releases.
If the link provided does not work or was not provided by you, we recommend checking the "Releases" section of this repository for the latest updates.
📦 Features
Here are some key features of the ffmpeg-mcp repository:
MCP Server Compatibility: Built specifically to work seamlessly with FFmpeg.
Enhanced Performance: Optimized for efficiency and speed in media processing tasks.
Secure Communication: Implements secure communication protocols for data transfer.
Scalable Architecture: Designed to handle large volumes of media processing requests.
🛠️ Installation
To install the MCP server for FFmpeg, follow these steps:
Download the latest release from the provided link.
Execute the downloaded file to start the installation process.
Follow the on-screen instructions to complete the installation.
Start using the MCP server with FFmpeg for your media processing needs.
📜 Usage
Once installed, you can use the MCP server with FFmpeg by following these steps:
Configure the server settings based on your requirements.
Connect the server to your FFmpeg environment.
Start sending media processing tasks to the MCP server for execution.
Monitor the progress and performance of the server through the provided interface.
📈 Roadmap
Future updates for the MCP server in this repository may include:
Integration with additional media processing tools.
Enhanced security features for data protection.
Performance optimizations for faster processing speeds.
Improved scalability for handling larger workloads.
🤝 Contributing
If you are interested in contributing to the development of the MCP server in this repository, please follow these guidelines:
Fork the repository and make your desired changes.
Submit a pull request with a detailed description of the modifications.
Your contributions will be reviewed by the repository maintainers for potential inclusion.
📧 Contact
If you have any questions, feedback, or suggestions regarding the MCP server for FFmpeg in this repository, please feel free to reach out to us at developer@example.com.
By following this README, you can quickly get started with the ffmpeg-mcp repository and leverage the MCP server's capabilities for your media processing tasks. For more detailed information, explore the repository's files, documentation, and releases. Thank you for your interest in our project! 🎉
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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