MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
Description from the maker (original English)
Lets an AI agent edit vertical 9:16 video ads from your footage, a reference ad or an idea.
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We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"adanvil": {
"command": "uvx",
"args": [
"adanvil",
"mcp"
]
}
}
}Run this in your terminal
claude mcp add adanvil -- uvx adanvil mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"adanvil": {
"command": "uvx",
"args": [
"adanvil",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"adanvil": {
"type": "stdio",
"command": "uvx",
"args": [
"adanvil",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
The registry name was verified with the GitHub account maxbuildog, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
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It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.4.0) reached the registry 18 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
An MCP server retrieving transcripts of YouTube videos
GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.
AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
An MCP server that lets an AI agent edit vertical 9:16 video ads.
AdAnvil works with Claude Code, Codex, Cursor or any MCP client. It builds ads from a brand's own footage, from a reference ad, or from an idea. The agent judges (which clips, what the text says, whether the result looks right). AdAnvil renders deterministically and checks the result: the same storyboard always gives the same video.
It is not tied to a niche. The recipes and the editing guide speak in general terms (product, benefit, problem, target customer) with examples from several niches: coffee, fashion, fitness, pets, software, home, beauty.
The demo above was made from the CC0 demo pack in examples/demo/ (four Wikimedia Commons clips, a product still and a fictional brand called Ember & Oak). No footage is stored in this repository: the pack is fetched on demand.
pill, meme, outline, outline_soft, box, label, comment) with emoji support.start/end windows; a sentence end the voice repeats is cut and flagged.sfx overlays and audio.sfx_auto at the cuts and captions, and cuts that snap to the beat of the music.story: "offer") for announcements and lead-collection ads that show the product from the first second.Pick one path. The first is the simplest.
Download adanvil- .mcpb from the latest release, double-click it, and click Install in Claude Desktop. It asks for a projects folder and, optionally, a Freesound key (stored securely). You still need ffmpeg 6.1 or newer on your computer (brew install ffmpeg, winget install Gyan.FFmpeg, sudo apt install ffmpeg); if it is missing, the extension says so with the command for your system.
git clone https://github.com/maxbuildog/adanvil && cd adanvil
docker build -t adanvil .
The image has ffmpeg 6.1, runs as a non-root user and speaks MCP over stdio. Mount the folder that holds your projects at /projects; the agent must give tool calls paths under /projects (for example /projects/my-ad): the image sets ADANVIL_PROJECTS_DIR=/projects, so any other folder, and /projects itself, is refused.
# Claude Code
claude mcp add adanvil -- docker run -i --rm -v "$PWD:/projects" adanvil
On Linux add --user "$(id -u):$(id -g)" so the files written to the folder belong to you.
Codex, in ~/.codex/config.toml:
[mcp_servers.adanvil]
command = "docker" args = ["run", "-i", "--rm", "-v", "/home/you/ads:/projects", "adanvil"] tool_timeout_sec = 900 default_tools_approval_mode = "approve" # needed with approval_policy = "never" (codex exec)
Cursor, in .cursor/mcp.json:
{
"mcpServers": { "adanvil": { "command": "docker", "args": ["run", "-i", "--rm", "-v", "/home/you/ads:/projects", "adanvil"] } } }
The image holds the base package only. Transcription is available with docker build --build-arg EXTRAS=transcribe -t adanvil:transcribe .. The voice, vision and music-generation environments (adanvil voice-setup, vision_setup, music-setup) are not included: they need gigabytes of models, so use the uvx path for them.
Requirements: Python 3.11 or newer, uv (it provides uvx), and ffmpeg 6.1 or newer, built with libzimg, on your PATH (check with ffmpeg -version; if it lives elsewhere, set ADANVIL_FFMPEG and ADANVIL_FFPROBE to the full paths).
# 1. Check the install (the first run downloads the package)
uvx --from adanvil adanvil --version
claude mcp add adanvil -e ADANVIL_PROJECTS_DIR="$HOME/AdAnvil" -- uvx --from adanvil adanvil-mcp
ADANVIL_PROJECTS_DIR (recommended) is the folder that holds your projects: the server then refuses any project outside it. Projects are subfolders of it (for example ~/AdAnvil/summer-ad). See SECURITY.md.
The package is named adanvil. It installs two commands: adanvil (command line) and adanvil-mcp (the MCP server over stdio). uvx adanvil-mcp alone does not work, because uvx looks for a package with that name: always add --from.
The agent creates the project folder and tells you where to drop your files: footage in footage/, the reference ad in reference/, music in music/, logo and end card in brand/. It learns the method from the editing_guide tool and from AGENTS.md.
Codex, in ~/.codex/config.toml:
[mcp_servers.adanvil]
command = "uvx" args = ["--from", "adanvil", "adanvil-mcp"] env = { ADANVIL_PROJECTS_DIR = "/home/you/AdAnvil" } tool_timeout_sec = 900 # indexing long footage can take minutes default_tools_approval_mode = "approve" # needed with approval_policy = "never" (codex exec)
Without default_tools_approval_mode, a non-interactive codex exec run rejects every AdAnvil tool call with "MCP tool call requires approval".
Cursor, in .cursor/mcp.json (project) or ~/.cursor/mcp.json (global):
{
"mcpServers": { "adanvil": { "command": "uvx", "args": ["--from", "adanvil", "adanvil-mcp"], "env": { "ADANVIL_PROJECTS_DIR": "/home/you/AdAnvil" } } } }
Any other MCP client (generic stdio entry):
{
"mcpServers": { "adanvil": { "type": "stdio", "command": "uvx", "args": ["--from", "adanvil", "adanvil-mcp"], "env": { "ADANVIL_PROJECTS_DIR": "/home/you/AdAnvil" } } } }
Restart the client after editing its config, then ask the agent to call recipe_list. Nine recipes means the server is connected.
Optional: reference transcription needs the transcribe extra (faster-whisper). Use --from "adanvil[transcribe]" in place of the plain --from value above.
The demo needs a checkout, because the fetch script lives in the repository:
git clone https://github.com/maxbuildog/adanvil
cd adanvil uv sync uv run python examples/demo/fetch_demo.py ./demo-project uv run adanvil index ./demo-project --source footage
Then register the MCP server and ask your agent to make an ad from ./demo-project (give it the absolute path), or follow the command line walkthrough in examples/demo/README.md.
Everything lives in one project folder: footage/, brand/, music/, reference/, renders/, cache/, plus adanvil.json, brand.yaml and catalog.json. Every tool takes the absolute project_dir; every other path is relative to it and cannot escape it.
storyboard_validate checks the storyboard against the recipe rules, then render_start renders it in the background.qc_run checks the file and returns control images that the agent looks at before delivering; ad_score scores its creative quality (aim for 80 or more).Details: docs/ARCHITECTURE.md for the pipeline, docs/EDITING_GUIDE.md for the editing doctrine, and AGENTS.md for the step-by-step guide an agent follows (same content as skill/SKILL.md, the Claude Code skill version).
The result depends on the model that drives AdAnvil. Reference analysis and shot choice are judgement work: use the strongest model you can (for example Claude Opus) for them. Smaller models execute the steps well, but they tend to copy the surface of a reference (same words, same shots) instead of rebuilding its logic for your brand.
| Tool | What it does |
|---|---|
project_init | Create an AdAnvil project skeleton. |
project_read_text | Read one of AdAnvil's own text files in the project (licence notes, JSON, YAML; at most 1 MB). |
brand_onboard | Read a brand's site, then write the brand profile from the answers. |
brand_get | Read the brand profile and brief. |
library_index | Index or refresh footage (long runs become detached jobs; refresh_shots measures the cuts again). |
library_view | One catalogue entry with its contact sheet. |
library_annotate | Save what the agent saw in a clip (description, people, emotion, quality, exclusion). |
library_search | Search the indexed footage by text (including what the clips say), tags, people, emotion, duration, or by sentence (query, with the vision environment). |
speech_view | Compact phrases (with flags), fillers, restarts and clean cut segments of one clip's speech. |
speech_takes | The best take of each line across several recordings of a script. |
speech_refine | Transcribe chosen clips again with a stronger model and the brand vocabulary (detached job). |
library_overview | Overview of the footage folders without indexing, plus a mosaic. |
frame_view | The full-resolution frame ON SCREEN at t (floor(t * fps), a t within 1 ms of a frame boundary is that frame) or at frame, with its exact time. |
reference_decode | Decode a reference video into shots, pacing, text, speech and sheets. |
reference_breakdown | Save the agent's shot-by-shot breakdown of the reference. |
recipe_list | List the ad recipes with purpose and duration. |
recipe_get | One full recipe with its rules and an example. |
editing_guide | The editing doctrine for footage, copy, rendering and QC. |
storyboard_schema | The JSON Schema of the storyboard. |
| storyboard_save | Save a storyboard as storyboards/ .json once; later tools take that
Only the beginning is shown; read the rest in the repo