"Cinema 4D" matching MCP servers:

Enables AI clients to read, search, write, tag, backlink, and render Markdown notes stored on a NAS through the local API of the Markdown Reader & Editor desktop app.

Enables semantic search and retrieval from your local markdown brain (Remember.md) via MCP tools, running entirely offline with local embeddings.

An MCP server that exposes an append-only, SQLite-backed event log for parallel research agents, providing append_delta, pull_deltas, and list_tasks tools over stdio.

Gives Claude persistent memory across sessions by recalling stored embers through graph-aware retrieval that fuses semantic embeddings, BM25 lexical search, engagement signals and PageRank-style traversal, with superseded memories hidden unless historical recall is requested. It also archives full session transcripts so past conversations and compacted context remain searchable.

Enables coding agents to query a temporal knowledge graph derived from a beads issue tracker via read-only Cypher queries, exposing current rules, supersession chains, and provenance without LLM API keys.

A local-first, LLM-agnostic MCP server that lets you ask hard questions about your documents, media, and code, and get traceable answers entirely offline.

Provides a unified multi-agent coordination layer with shared memory, wiki, skills, and file management via MCP tools, enabling agents to access and collaborate on resources in a single Python process.

MCP server that enables full-text search and link navigation over Markdown files as a knowledge graph.

Model-agnostic, agent-ready Obsidian MCP server with RBAC, SLSA provenance, and native search. 163 tools across 31 domains, multi-vault, pluggable embeddings.

Provides LLMs with access to molt-md, an encrypted markdown document hosting service, for managing and reading secure knowledge bases. It enables users to create, update, and organize encrypted documents into workspaces with full version control and end-to-end encryption.

Provides persistent memory for AI assistants via MCP, enabling them to store and recall facts, preferences, and tasks across conversations using either local file storage or a cloud backend with semantic search.

Enables persistent, project-scoped memory for OpenAI Codex Desktop and CLI on Windows, capturing prompts and responses through managed hooks, with federated recall across projects and optional local graph extraction.

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.

Connects AI assistants to a persistent memory engine with Neo4j knowledge graph and ProMem extraction, enabling long-term context and associative memory across chats and workspaces.

Provides AI agents with a persistent, live-synchronized memory bank for a builder's identity, projects, tech stack, snippets, vision, and bio materials, allowing them to read and update this context across sessions. Supports cloud-backed Neon PostgreSQL storage, encrypted secrets, and optional local-only operation.

MCP server for the operbots panel, enabling AI assistants to manage tasks, Telegram bots, flow scenarios, conversations, knowledge base, and AI connections with the user's permissions.

A local, zero-cost MCP server that fact-checks your Markdown/Obsidian notes using a local LLM (via Ollama) and free sources like Wikipedia and DuckDuckGo, with no cloud API or API keys.

Enables AI assistants to programmatically interact with Google NotebookLM, allowing them to create and manage notebooks, add sources, query content, generate audio/video, and perform research tasks through natural language commands.

Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.

Enables humans and AI coding agents such as Claude Code, Codex, Cursor, and Gemini CLI to pass in-progress work between each other through shared, versioned, authenticated context that preserves decisions, artifacts, evidence, review history, and authorship. It exposes identity, search, retrieval, review, continuation, publishing, and portability tools over a Streamable HTTP endpoint.