"A guide on how to edit videos" matching MCP servers:

MCP server that provides secure access to a serverless Markdown wiki on AWS, enabling AI assistants to read, search, and edit pages with space-level permission checks and Bedrock-powered semantic search.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Enables AI agents to provide e-commerce customer service with product search, RAG-based FAQ answers, human handoff, and automated security auditing with a review workflow.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables Claude to search and read documentation, ask RAG AI questions (one-shot or in a conversation), and create/edit documents in the Meet Rupert knowledgebase, including handling image attachments.

Provides tools for searching Agent8 game development code examples and system prompts to assist in Agent8 SDK development.

Enables building a local, searchable knowledge base from YouTube videos, with research, transcript retrieval, and timestamped citations.

Enables AI agents to perform keyword, semantic, hybrid, and SQL retrieval over data stored on object storage or local paths, using a local embedding model without requiring an API key.

Exposes WAI-ARIA Authoring Practices Guide (APG) patterns, providing narratives, keyboard guidance, and official source code examples for accessible widgets. It includes tools for listing patterns, retrieving specific specs, and performing semantic searches via Ollama.

Enables AI assistants to search a curated database of 200+ AI tool cards with pricing, platforms, use cases, and source links, so users can ask which tool to use for a task and get grounded recommendations.

Turn YouTube, TikTok, X videos and websites into structured data. Skip the hassle of video transcription and data scraping. Our APIs help you build better software and AI products faster.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

Memory vaults on your own machine, one per life domain, retrieved with a local BM25 index fused with vector search, where the human decides which vaults a model may answer from. Requires the Mnemosyne OS desktop app running locally; it also reports what other coding agents on the same machine are doing.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

A local RAG MCP server that indexes project documentation and code into ChromaDB for semantic search, supporting multiple tech stacks and deployment modes.

Exposes semantic search over a folder of indexed PDF guides as a tool that Claude Code or any MCP client can call. Returns the closest matching passages with relevance scores, source file and page citations, so the client can answer questions about the guides while knowing when nothing relevant was found.

Enables AI assistants to search and edit Apple Notes on macOS using semantic search, with safe write operations and full local indexing.

Enables AI agents to semantically search, ask questions about, and analyze videos, with persistent memory and social media import capabilities.

Enables asking natural language questions about local videos, using Whisper transcription, ChromaDB vector storage, and Groq for timestamped answers via MCP.

Enables AI agents to fetch one or many web pages concurrently and receive clean, LLM-ready Markdown with navigation, ads and boilerplate stripped away, plus extracted metadata such as title, description, author, Open Graph tags and canonical URL. It runs locally over stdio with no API key or account, requiring only a single command.

A Model Context Protocol server that enables AI models to perform real-time internet and knowledge searches through Higress, enhancing model responses with up-to-date information from Google, Bing, Arxiv, and internal knowledge bases.

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

Enables querying a compressed knowledge graph of the NVIDIA AI developer stack, allowing users to traverse declared dependencies, prerequisites, and relationships across 20 domains via read-only MCP tools like query_ckg, get_prerequisites, and search_concepts.

Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.

Enables AI coding agents to intelligently index and search codebases with sub-20ms retrieval, 8x memory compression, and cross-encoder reranking via MCP stdio.

A local MCP server that indexes files in a directory using Gemini Embedding 2, enabling AI agents to perform semantic search over local documents.

Enables Claude to build and query a local, folder-based knowledge base with hybrid vector and BM25 search, save and edit notes, ingest Markdown/text folders with source citations, and browse or restore version history — all running embedded on a laptop with no server or API key.

Provides unified semantic search across all Plesk documentation sources (Admin Guide, REST API, CLI, PHP SDK, JS SDK) for extension developers, with sub-second latency and AI-synthesized answers.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Enables natural language search and interaction with video content through three tools: ingesting videos to a Ragie index, retrieving relevant video segments based on queries, and creating video chunks from specific timestamps.