"Creating or obtaining a market map for AI video generation" matching MCP servers:

An MCP server that reduces AI coding agent token usage by 80-99% through a queryable knowledge graph of code, change tracking, and persistent memory across sessions.

MCP server that provides a navigable map of an Obsidian vault for coding agents, with tools to outline notes, fetch sections, query frontmatter, and grep content, reducing token usage compared to reading whole notes.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

AI building world

Enables a small local LLM to build and maintain a project map by offloading event ID/date generation, batching, synchronization, recovery, and finalization to the server, so the model only decides what to read and how to classify.

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

An experimental MCP server that explores neuroscience-inspired approaches to software development analysis, featuring associative memory, context-aware caching, and cross-language API detection.

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.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Generates structured project memory and exposes MCP tools for AI coding agents to query topology, read relevant files, and log changes in a repository.

Enables AI models to securely and structurally assimilate experiences, thoughts, and reasoning processes across different AI systems, fostering intellectual collaboration and evolutionary dialogue.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

An MCP server for managing work logs, research results, and task checkpoints to enable seamless collaboration and state recovery between AI agents. It provides a persistent memory layer for tracking project history and resuming workflows across different sessions or tools.

Provides durable project context for coding agents, including project maps, session history, and explicit memories, all stored locally.

Enables AI agents to search and traverse the 2022 revised Korean elementary curriculum across 11 subjects, 620 achievement standards, and 2,293 prerequisite relationships. It also supports reverse diagnosis of foundational learning gaps and generates personalized remedial learning roadmaps.

Persists Claude Code sessions to Notion as team memory, enabling queryable recall of decisions, work state, and project architecture.

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

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

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.

Provides a compressed knowledge graph of the NVIDIA AI developer stack for deterministic traversal, enabling agents to answer questions about dependencies and prerequisites with minimal tokens.