What is video-url-analyzer-mcp AGENTS.md?

Instructions for a server that analyses videos and photos from YouTube, TikTok, and Instagram using Google’s Gemini API, an AI service for processing media. It supports videos, photo carousels, transcripts, questions, tutorials, saved context, and evidence assets.

Use it to analyse social-media videos or photo posts, extract transcripts and moments, ask questions about media, and save or retrieve supporting evidence.

It provides a defined way to fetch and analyse media from several social platforms, including both video sound and ordered carousel images.

How u2n4/video-url-analyzer-mcp configures Codex and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to u2n4/video-url-analyzer-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/u2n4/video-url-analyzer-mcp/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/u2n4/video-url-analyzer-mcp

Made for: Codex, OpenCode.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/u2n4/video-url-analyzer-mcp/agents-md/github.svg)](https://agentmods.dev/instructions/u2n4/video-url-analyzer-mcp/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/u2n4/video-url-analyzer-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/u2n4/video-url-analyzer-mcp/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/u2n4/video-url-analyzer-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/u2n4/video-url-analyzer-mcp/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 1,982 This file is loaded in full into every session.

When invoked 1,982 The same file — it is already loaded in full.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.01982	$0.01982
Opus 5.5	$0.00793	$0.00793
Sonnet 5.5	$0.00396	$0.00396
Haiku 4.5	$0.00198	$0.00198

Measured today against content hash 81800c8ae510, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

video-url-analyzer-mcp AGENTS.md scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

- Image downloads use `_download_media_url`: urllib (system DNS, 3

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

video-url-analyzer-mcp CLAUDE.md — 100% identical, 4 lines differ

AGENTS.md · 160 lines

How it starts

The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Video Analyzer MCP Server

Project Purpose

MCP server for video/photo analysis using Google's Gemini API. Supports YouTube (direct, no download), TikTok (videos + photo slideshows), and Instagram (Reels, video posts, and photo carousels). Downloaded media is uploaded to the Gemini Files API and analyzed with full visual (and, for videos, audio) coverage.

Architecture

server.py: Main MCP server with 18 tools: the original analysis/transcript/Q&A/tutorial tools, structured moment/segment tools, saved video context tools, local evidence asset tools, and cache tools.
Pipelines:
- YouTube: Direct analysis: the YouTube URL is sent as a FileData part (no download).
- TikTok/Instagram video: Fast-path API/scrape → yt-dlp fallback → upload to Gemini Files API → analyze → cleanup.
- TikTok/Instagram photo carousel: Scrape image URLs from page/API → download each image → upload ALL images to Gemini → analyze as a single carousel with ordered slides → cleanup.
_download_video(url) returns list[str] (one path for a video, many for a carousel). _analyze_downloaded handles both transparently.
YouTube frame/clip extraction has a fast local path using yt-dlp -g stream URLs + ffmpeg. If source.mp4 is missing but a saved context has the original YouTube URL, asset tools can still extract frames/clips.
FastMCP for MCP protocol (stdio transport).
google-genai SDK for Gemini API interaction.

Media download strategy

TikTok photo posts: tikwm.com API returns data.images[] of image URLs.
Instagram photo posts: page HTML is scraped; the carousel_media / edge_sidecar_to_children JSON block is isolated to avoid picking up thumbnails of suggested/related posts. Falls back to whole-page scan if the block isn't present. Hard cap at 20 unique images.
Image downloads use _download_media_url: urllib (system DNS, 3 retries) → curl_cffi impersonate fallback. urllib-first was added because curl_cffi's bundled resolver intermittently fails to resolve some *.fna.fbcdn.net CDN shards on Windows while system DNS works.
All temp files deleted in a finally block; uploaded Gemini files are also deleted after the analysis call returns.

Read the full file on GitHub · 160 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

today Changed · +1 lines · +56 tokens per session 81800c8ae510
2mo ago First seen · 159 lines · 1,926 tokens per session scan A d3d1007ed2f5

Subscribe to this mod's changes

video-url-analyzer-mcp AGENTS.md is an instructions file published in the GitHub repository u2n4/video-url-analyzer-mcp (4 stars, last pushed yesterday), licensed MIT. It adds 1,982 tokens to every session, about $0.0079 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Creative and media Data and AI

video-url-analyzer-mcp AGENTS.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
video-url-analyzer-mcp AGENTS.mdu2n4/video-url-analyzer-mcp	Instructions file	4	yesterday	MIT	A	1,982	Codex, OpenCode
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	9,719	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	yesterday	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	yesterday	MIT	A	4,680	Codex, OpenCode
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

video-url-analyzer-mcp: Instructions file for Codex