"Information or resources about video editing" matching MCP servers:

45-tool MCP server for video analysis, deep research, content extraction, web search, and Weaviate knowledge storage. Powered by Gemini 3.1 Pro.

Self-hosted video intelligence pipeline that turns video into structured, citable, queryable JSON scene graphs, enabling AI assistants to analyze and search across video libraries without re-processing.

Extracts content from Douyin, Bilibili, Xiaohongshu, and Zhihu, and generates intelligent knowledge graphs.

Enables querying and retrieval of structured video summaries from a SQLite knowledge base through Claude or Cursor, supporting search, precise matching, and tag-based filtering.

A self-hosted MCP server for building a reusable AI-video reference library. It stores named Elements as structured reference images and provides them to MCP-compatible agents via @tags.

Turn any YouTube video, article, PDF, or image into a reusable Claude Code skill — without leaving your editor.

Provides tools for managing project knowledge graphs, enabling structured representation of projects, tasks, milestones, resources, and team members.

An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.

Universal MCP knowledge server for LLM agents, powered by local RAG, providing domain-specific best practices and playbooks across software engineering, marketing, video editing, and other knowledge areas.

Your AI forgets everything between sessions. Rekindle fixes that. For Claude Code users who lose time re-explaining project context every session.

A local MCP server that maintains a persona profile (contact library) for AI assistants, allowing them to create, search, retrieve, supplement, and correct information about people mentioned in conversations via tools like search_profiles, get_profile, create_profile, update_profile, add_facts, update_fact, and delete_fact.

A personal knowledge hub MCP server that enables LLMs to query structured information about Akib Dabgar's skills, experience, projects, and goals using Dynamic Context Retrieval for efficient context usage.

Gives AI coding agents persistent, evolving knowledge about a codebase, enabling them to store and retrieve observations about architecture, conventions, gotchas, and recent work context.

Exposes tools to ingest FigJam boards, retrieve context summaries, and answer free-form questions about the board content.

MCP server that exposes a resume as callable tools and resources, enabling AI agents to query experience, skills, projects, and contact information via natural language.

Description: Persistent memory for AI agents with rollback, audit trails, semantic search, and knowledge graph. Zero-config local SQLite or cloud API. 23 tools, 6 resources, 3 prompts.

Connects AI assistants like Cursor and Claude to your local Daylens timeline so they can answer questions about your past work sessions, no cloud or API keys needed.

Enables storing and querying structured information about software code entities (classes, functions, files) and their relationships (calls, imports) along with qualitative observations like design decisions and change rationale.

Enables AI agents to record, recall, correct, and forget evidence-backed factual claims with temporal history, while explaining whether remembered information is current, historical, or contested.

Exposes a knowledge base to LLM clients through tools, resources, and prompts.