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"A platform for sharing photos and videos on social media" matching MCP servers:

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    Social media analytics toolkit that analyzes profiles, scores engagement, detects trending topics, researches hashtags, generates content calendars, and benchmarks against competitors. 6 tools across all major platforms.
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    MIT
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    AI-powered social media analytics tools that enable profile analysis, engagement scoring, trend detection, and hashtag research across major platforms in real time.
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    MIT
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    This MCP fetches trending data from platforms like YouTube, TikTok, and Instagram Reels using various scraping and API techniques. It's designed to run under FastMCP and exposes tools that can be consumed via a local CLI or claude.
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    17
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    Checks username availability and retrieves account information across major social media platforms including YouTube, TikTok, Threads, X (Twitter), and Instagram, perfect for brand name qualification and competitive research.
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    MIT
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    An MCP server for image and video processing that allows AI assistants to resize, convert, compress, and analyze media files locally without API keys. It supports a wide range of formats and provides tools for tasks such as metadata removal, frame extraction, and video conversion.
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    13
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    MIT
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    AI-powered social media posting across 14 platforms. Post to Twitter, Instagram, TikTok, Facebook, LinkedIn, YouTube and more with one command. AI adapts content per platform, schedules posts, and generates 30-day content calendars.
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    6
    MIT
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    An MCP server for cross-platform social media research that enables scraping, semantic search, and analysis across seven major social platforms. It uses Apify for data retrieval and stores results in a local database for AI-powered trend detection and profile analysis.
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    14
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    A Model Context Protocol (MCP) server that empowers LLMs to search across specific social media platforms using the industry's best search engines.
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    Provides tools for AI clients to scan, match, and scrape TMDB metadata for local media files, with safe sidecar writing or full Jellyfin-style reorganization including file renaming and NFO generation.
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    MIT
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    Enables LLM clients to programmatically generate videos using Plainly's API by listing available video templates, retrieving template parameters, submitting render requests, and checking render status.
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    MIT
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    MCP server for extracting transcripts from Instagram videos using AssemblyAI, enabling timestamped transcriptions with speaker labels through natural language.
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    17