"YouTube fashion content finder for trends and style guides" matching MCP servers:

Enables AI-powered YouTube content management using Claude Code to orchestrate research, scripting, and scheduling via Google Sheets, Docs, and Drive.

Enables style verification and CSS inspection through Chrome DevTools Protocol, using JSON specs to check computed styles, find elements, and interact with pages.

A fashion vertical MCP server providing tools for product SEO audits and trend analysis to integrate with AI agents like Claude, Cursor, and Codex.

Extracts YouTube video metadata, titles, and descriptions along with transcripts generated from subtitles or OpenAI Whisper speech-to-text. This server enables users to retrieve and analyze detailed video content directly within MCP-compatible environments.

A tiny local MCP server that learns a user's conversational style, catchphrases, dialect markers, emoji habits, tone preferences, and concrete collaboration preferences without storing private memories.

Analyzes a codebase to extract its coding conventions and exposes tools via MCP for AI agents to retrieve live style rules, ensuring generated code matches the project's unwritten style.

Provides deterministic design style recommendations and structured tokens for AI content generation, with 30 curated styles including color palettes, typography, and visual directives.

An MCP server for generating Chromium-style QR codes with circular modules, rounded locators, and an optional center image (Dino logo or custom).

Provides data-driven cover letter coaching by analyzing tone, keywords, and structure using a database of successful examples and corporate strategy reports.

This server that distributes a single piece of content across 8+ channels (DEV.to, Hashnode, GitHub Discussions, Reddit, Bluesky, LinkedIn, Medium, Twitter) with automatic platform-specific adaptation, idempotent publishing, per-community anti-spam rules, and centralized state management.

Finds marked hiking routes from OpenStreetMap filtered by locally-computed elevation gain, distance, loop shape, and car/chairlift access, providing consistent and tunable metrics instead of third-party scraped data.

Fast, minimal YouTube 'watch' engine for AI agents that extracts clean transcripts, searches, and slices any YouTube video without requiring an API key.

Enables natural language code search across multiple local Git repositories, allowing users to register projects, search for code, and explore file structures through Claude.

Enables editorial content QA over the Model Context Protocol with four local tools that analyze readability, AI-sounding language, SEO on-page factors, and produce full reports where every finding includes an actionable fix.

Provides content agents with structured search over editorial guidelines, visual templates, and past post outcomes, enabling auditable proposals that cite rule IDs.

Reduces thousands of security findings to the smallest set of high-impact remediation actions, with fail-closed safety gates, typed execution plans, and verification that refuses false all-cleans.

Enables AI assistants to fetch YouTube video transcripts and metadata without sign-in, and access signed-in users' own YouTube account data such as subscriptions, playlists, uploads, and caption tracks via OAuth 2.0.

Maps any source asset (e.g., long video, podcast, blog) to all platform formats (TikTok, Reels, Shorts, etc.) with per-platform adaptation rules, enabling a 'film-once-cut-many' content workflow.

Enables Claude Code to access YouTube features such as video transcripts, search, metadata, channel info, playlists, comments, trending videos, engagement analytics, chapters, SponsorBlock, and most-replayed heatmaps.

Enables YouTube search, video details, channels, playlists, and comments scraping via MCP, no API key required.