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mcp-ffmpeg

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MCP-FFMPEG

An MCP (Model Context Protocol) server and CLI for running FFmpeg jobs via a job queue with configurable parallel workers.

Features

  • Job queue — Enqueue video jobs; workers process them in the background.

  • Parallel workers — Run multiple jobs at once (number set in config).

  • Two interfaces

    • CLI — Interactive menu to pick an action and enter parameters.

    • MCP server — Tools for AI assistants (e.g. Claude Desktop) to enqueue and check jobs.

  • Actions

    • Trim — Cut a segment from a video (start time + duration).

    • Change video format — Convert to another container (e.g. mp4 → mkv) without re-encoding.

    • Change resolution — Convert to another resolution, height and width provided by user.

    • Change Subtitle format — Convert to another subtitle format (e.g. srt -> vtt).

    • Extract Audio - Extract audio from an input video file

    • Extract video transcript - Extracts the transcript of a video file

  • Caching — Same inputs produce the same job ID; completed jobs are reused unless force_run is used.

Related MCP server: MCP FFmpeg Helper

Requirements

  • Python 3.13+

  • FFmpeg — Must be on your system PATH or set via FFMPEG_PATH (see Configuration).

Installation

Choose one of the following:

Option 1: Install from PyPI

pip install mcp-ffmpeg

Or with uv:

uv add mcp-ffmpeg

Option 2: Clone the repository

git clone https://github.com/priyanshum143/MCP-FFMPEG.git
cd MCP-FFMPEG

Then install from the project root:

# With uv
uv sync

# Or with pip (editable install)
pip install -e .

Configuration

  • FFmpeg path

    • Default: use ffmpeg from system PATH.

    • Optional: set env var FFMPEG_PATH to the full path of the FFmpeg executable (e.g. for Claude Desktop).

  • Worker and paths
    Edit src/MCP_ffmpeg/utils/variables.py (class CommonVariables):

    • PARALLEL_EXECUTIONS_ALLOWED — Number of jobs that can run at once (default: 3).

    • WORKER_RE_RUN_TIME — Seconds to wait between queue checks (default: 10).

    • OUTPUT_DIR / LOGS_DIR — Where job outputs and logs are stored (default: outputs/ and logs/ under project root).

Running

CLI (interactive)

You get a menu: choose an action, enter the requested parameters. Jobs are enqueued and processed by background workers. Logs show which worker picked which job.

If you installed from PyPI:

mcp-ffmpeg-cli

If you cloned the repo:

# With uv (from project root)
uv run python -m MCP_ffmpeg.main

# Or after pip install -e .
mcp-ffmpeg-cli

MCP server (e.g. Claude Desktop)

Use the mcp-ffmpeg command so the MCP server runs over stdio.

If you installed from PyPI: use mcp-ffmpeg in your MCP config.

If you cloned the repo: after pip install -e ., use mcp-ffmpeg the same way. If you used uv sync, use uv run mcp-ffmpeg in the terminal, or in your MCP config use the path to your venv’s mcp-ffmpeg script so the server runs in that environment.

Add this to your %APPDATA%\Claude\claude_desktop_config.json (Windows) or the equivalent config for your MCP client:

{
  "mcpServers": {
    "mcp-ffmpeg": {
      "command": "mcp-ffmpeg",
      "env": {
        "FFMPEG_PATH": "C:\\path\\to\\your\\ffmpeg.exe"
      }
    }
  }
}

On macOS/Linux, use your normal config path and set FFMPEG_PATH to the path of your ffmpeg binary if needed.

Project layout

src/MCP_ffmpeg/
├── main.py           # CLI entrypoint
├── mcp_server.py     # MCP server entrypoint + tool definitions
├── actions/          # FFmpeg actions (trim, change format)
├── jobs/             # Job queue, manager, worker
└── utils/            # Logging, paths, CLI helpers

  • outputs/ — One folder per job (by job ID), containing job_details.json, output file, and optional ffmpeg_logs.log.

  • logs/ — Application logs.

License

No license required, Clone/Fork the repo and enjoy.

Author

Priyanshu CSE 2025 Graduate | Software Engineer at Amagi Media Labs

Feel free to reach out for collaborations or if you encounter any issues!

Available Tools

8 tools
get_job_resultA

Retrieve the final output of a completed job.

This tool should only be called after the job status is 'success'. If the job is still 'queued' or 'running', this will return the current status. If the job has 'failed', it will return the error details.

ParametersJSON Schema
NameRequiredDescriptionDefault
job_idYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses all behavioral traits for different job states (success, queued/running, failed) without relying on any annotations. It fully informs the agent of what to expect, compensating for the lack of annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with four sentences, front-loaded with the primary purpose. Each sentence adds essential information without redundancy, making it efficient for an AI agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, output schema present), the description covers all necessary contexts: when to use, what returns for each job status. The output schema handles return value details, so no further elaboration needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has one parameter (job_id) with 0% schema description coverage. The description does not mention or explain the job_id parameter at all, failing to add meaning beyond the schema's type and title. The tool description should have explained what job_id refers to and how to obtain it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves the final output of a completed job using the verb 'Retrieve' and specific resource 'final output of a completed job'. It distinguishes itself from sibling tool get_job_status by focusing on result retrieval rather than status checking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use the tool: 'only be called after the job status is success'. Also describes what happens for other statuses (queued/running returns status, failed returns error details), providing clear guidance on expected behavior in each scenario.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_job_statusB

Get the latest status for a job_id.

ParametersJSON Schema
NameRequiredDescriptionDefault
job_idYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It only implies a read operation ('Get') but does not disclose any behavioral traits such as required authentication, rate limits, or whether it is destructive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence that immediately states the purpose. No unnecessary words or information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, output schema exists), the description provides the basic purpose. However, it lacks context about what 'status' means or possible usage scenarios, making it minimally complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description adds no meaning beyond the schema's 'title: Job Id'. It fails to compensate for the lack of parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'latest status for a job_id', distinguishing it from sibling tool 'get_job_result' which returns results, and from 'start_*' tools which initiate actions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like 'get_job_result'. Does not specify prerequisites (e.g., job must exist) or conditions for use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

start_audio_extractionA

Enqueue a EXTRACT_AUDIO job. Extract the audio from a given video and in the format requested by user Returns immediately with job_id + status.

input_file: path to input video file (must exist) target_format: target audio format (defaults to mp3) force_run: if True, run even when a cached result exists for the same inputs

ParametersJSON Schema
NameRequiredDescriptionDefault
input_fileYes
target_formatNomp3
force_runNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses immediate return with job_id and status, and cache behavior via force_run. Lacks details on permissions, file overwrites, or error scenarios.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short paragraphs: action/return, then parameter definitions. No unnecessary text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, parameters, and return value. Lacks output schema details but mentions job_id and status. Sufficient for a simple enqueue job.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, description adds value by explaining input_file requirement, target_format default, and force_run purpose. Compensates well for lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it extracts audio from video in user-requested format. Differentiates from sibling tools like start_trim or start_video_transcription.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides basic usage context (input_file must exist, force_run bypasses cache) but does not explicitly compare to alternatives or specify when to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

start_change_formatA

Enqueue a CHANGE_FORMAT job. Converts video to another container (e.g. mp4, mkv, avi) without re-encoding. Returns immediately with job_id + status.

input_file: path to input video file (must exist) output_format: desired output format (e.g., mp4, mkv, avi); dot is optional force_run: if True, run even when a cached result exists for the same inputs

ParametersJSON Schema
NameRequiredDescriptionDefault
input_fileYes
output_formatYes
force_runNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.7/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. Description mentions non-re-encoding, caching behavior via force_run, and immediate return of job_id+status. However, it doesn't disclose potential side effects (e.g., overwriting files) or permissions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise: three sentences plus parameter explanations, front-loaded with core action. No redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers core functionality and parameters. Mentions async nature via 'Returns immediately with job_id + status'. Output schema exists, so return values not needed. Lacks details on output file location or persistence.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description fully defines each parameter: input_file must exist, output_format with examples and optional dot, and force_run behavior. This adds essential meaning beyond the schema titles.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool converts video to another container format without re-encoding, using specific verbs 'Enqueue' and 'Converts'. It distinguishes from siblings like start_change_resolution by focusing on container format change.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives like start_change_resolution or start_audio_extraction. Does not specify prerequisites or context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

start_change_resolutionA

Enqueue a CHANGE_RESOLUTION job. Converts the video resolution according to the user's provided height and width. Returns immediately with job_id + status.

input_file: path to input video file (must exist) height: desired height of the video width: desired width of the video force_run: if True, run even when a cached result exists for the same inputs

ParametersJSON Schema
NameRequiredDescriptionDefault
input_fileYes
heightYes
widthYes
force_runNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description reveals that the tool is asynchronous ('Returns immediately with job_id + status') and mentions caching behavior via force_run parameter. However, since no annotations are provided, more details about side effects (e.g., file creation, original file modification) would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with a front-loaded opening sentence followed by bullet-pointed parameter explanations. No redundant information; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema, the description appropriately summarizes return behavior. It covers all parameters and asynchronous nature. Missing explicit differentiation from siblings and potential prerequisites (e.g., supported video formats) but overall complete for a job-enqueueing tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All four parameters are described with meaningful explanations beyond the schema: input_file path with existence requirement, height/width as desired dimensions, and force_run caching override. Schema coverage was 0%, so the description fully compensates.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool enqueues a CHANGE_RESOLUTION job that converts video resolution according to provided height and width. It uses specific verb 'Enqueue' and resource 'CHANGE_RESOLUTION job', distinguishing it from siblings like start_change_format.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains what the tool does but does not explicitly state when to use this tool versus alternatives (e.g., start_change_format for format conversion). Usage is implied by the purpose but lacks explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

start_subtitle_format_changeA

Enqueue a CHANGE_SUBTITLE_FORMAT job. Converts the subtitle format according to the user's provided details. Returns immediately with job_id + status.

input_file: path to input video file (must exist) target_format: target subtitle format force_run: if True, run even when a cached result exists for the same inputs

ParametersJSON Schema
NameRequiredDescriptionDefault
input_fileYes
target_formatYes
force_runNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Since annotations are missing, the description carries full burden. It discloses the async behavior (returns immediately with job_id+status) and the force_run option for caching. However, it doesn't mention error handling or required permissions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the core action and return behavior. It lists parameters with brief explanations without unnecessary fluff. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists (though not shown), the description adequately explains the async return with job_id+status. Sibling tools for checking job results and status provide complete context for the workflow.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description adds meaningful context for parameters: 'path to input video file (must exist)' and 'target subtitle format'. The force_run parameter is also explained. This compensates for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it enqueues a job to convert subtitle format, using a specific verb and resource. This distinguishes it from sibling tools like start_audio_extraction or start_change_format.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus alternatives, such as start_change_format or other conversion tools. No conditions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

start_trimA

Enqueue a TRIM job. Returns immediately with job_id + status.

input_file: path to input video file (must exist) start_time: seconds (float) duration: seconds (float) force_run: if we need to force run a job or not ignoring cache

ParametersJSON Schema
NameRequiredDescriptionDefault
input_fileYes
start_timeYes
durationYes
force_runNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.1/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes asynchronous enqueue behavior and force_run's cache-ignoring effect. No annotations exist. Missing details on failure modes, resource consumption, or whether trimming modifies original file.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first states purpose and return, second lists parameters with inline clarifications. No redundant words, front-loaded with core action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all parameters, mentions return format, and assumes output schema handles details. Lacks mention of possible side effects or file size considerations, but sufficient for a simple trim tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage; description adds critical context: input_file must exist, times are floats in seconds, duration defined, force_run for cache bypass. Fully compensates schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Enqueue a TRIM job' with immediate return of job_id+status. It specifies the verb (enqueue/trim) and resource (video file), and differentiates from siblings like start_audio_extraction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use or when-not-to-use guidance. The parameter listing implies trimming video files, but alternatives like start_change_format are not contrasted. Lacks usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

start_video_transcriptionA

Enqueue a transcript job using Whisper on a video or audio file. Returns immediately with job_id + status.

input_file: path to input media file (must exist) model: Whisper model name (tiny, base, small, medium, large, turbo) language: language code (e.g. en, hi) output_format: transcript format (txt, srt, vtt, json, tsv) force_run: if True, run even when a cached result exists for the same inputs

ParametersJSON Schema
NameRequiredDescriptionDefault
input_fileYes
modelNosmall
languageNoen
output_formatNotxt
force_runNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully handles behavioral disclosure. It explains that the tool is asynchronous (returns immediately with job_id and status) and describes the force_run parameter's effect on caching. This is comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (two short sentences plus bullet-like parameter list) and front-loaded with the core function. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema and no annotations, the description covers the essential aspects: action, async behavior, return, and all parameters. It lacks details on error handling or file size limits, but overall it is sufficient for correct use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the tool description lists all five parameters with their meaning, valid values (e.g., model names, language codes, output formats), and constraints ('must exist' for input_file). This fully compensates for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool enqueues a transcription job using Whisper on video/audio files, distinguishing it from sibling tools like start_audio_extraction or start_change_format. The verb 'Enqueue' and resource 'transcript job' are specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for transcription tasks, but does not explicitly state when to use it versus alternatives like start_audio_extraction or get_job_result. No exclusions or prerequisites are mentioned.

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.

  1. 8 tool updatesv0.1.6
    • First observedget_job_result
    • First observedget_job_status
    • First observedstart_audio_extraction
    • First observedstart_change_format
    • First observedstart_change_resolution
    • First observedstart_subtitle_format_change
    • First observedstart_trim
    • First observedstart_video_transcription
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TDQS

A4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: status retrieval, result retrieval, and separate processing jobs for audio extraction, format change, resolution change, subtitle format change, trimming, and transcription. No overlap in functionality.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern: get_ for retrieval and start_ for job enqueuing. Underscores are used uniformly, making the naming predictable.

Tool Count5/5

With 8 tools, the server covers a reasonable set of common FFmpeg operations without being overwhelming. The count is well-scoped for a media processing server.

Completeness4/5

The tool set covers core operations like status check, result retrieval, audio extraction, format conversion, resolution change, subtitle conversion, trimming, and transcription. Missing tools for video compression, frame extraction, or concatenation, but these are minor gaps given the common use cases.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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