"A tutorial or tools for building 3D models in CAD software" matching MCP servers:

Enables coding agents to turn mechanical requirements into validated FreeCAD models with deterministic sizing from published standards, standard-part provenance, and automatic geometry validation.

Persistent memory and pattern learning for AI coding assistants via MCP.

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.

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.

Free Industry 4.0 / IIoT tools, no API key or account: lint MQTT Sparkplug B topics, check a Unified Namespace (UNS) for naming and hierarchy problems, and run an industrial AI-readiness diagnostic for manufacturing digital transformation and smart-factory projects. Works in Claude Code, Codex and any MCP client.

Provides customer lookup, transaction history, knowledge-base search, and ticket management tools to any MCP client.

Enables searching, reading, and appending change notes in an Obsidian vault through local MCP tools.

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.

A collection of MCP servers for Apple macOS apps (Mail, Contacts, Notes, Memory, Messages, Calendar, Reminders) enabling AI assistants to read, search, create, and update data via JXA, SQLite, and EventKit.

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

Extends VS Code Copilot with MCP tools for persistent semantic memory, code indexing and RAG search, sandboxed Python/shell execution, web fetch/search, dynamic tool management via a web UI, and structured multi-step reasoning.

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.

Enables natural language interaction with Obsidian vaults through an MCP server, providing hybrid search, file management, and AI-powered analysis.

Provides MSP support tools (ticket search, draft response, KB search, update) with a deterministic security guardrail that refuses to draft responses for security tickets based on content scanning, even if mislabeled.

Enables personal knowledge management through Claude Desktop, allowing users to capture thoughts, connect ideas, and reflect on thinking changes via natural conversation.

Enables semantic search across Apple Mail, Messages, Calendar, and Contacts on macOS using natural language queries. All processing happens locally with privacy-first vector indexing for fast similarity search.

Provides a scalable knowledge graph implementation for Model Context Protocol using Elasticsearch, enabling AI models to store and query information with advanced search capabilities, memory-like behavior, and multi-zone architecture.

A MongoDB Atlas-based Notion-style knowledge base management MCP server that supports user workspaces, project management, page CRUD with tree structure, and text search.

Enables agents to optimize context-window attention by reordering retrieved documents so critical evidence lands at prompt boundaries, mitigating lost-in-the-middle degradation. It combines BM25 lexical scoring, dense vector similarity, reciprocal rank fusion, knowledge-graph expansion, and recency-decay memory caching, exposed as a zero-dependency MCP stdio server.

Enables agents to run hybrid retrieval, reciprocal rank fusion, knowledge graph expansion, semantic caching, and context reordering so critical evidence is placed at prompt boundaries to reduce lost-in-the-middle degradation.

Enables Codex projects to read Git-backed kickoff, acceptance, handoff, progress, and next-step context, and to append validated, audited progress events through scoped MCP tools. This supports visible agent progress, resumable sessions, and safer handoffs.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

An MCP server that provides shared memory, kanban board, and agent registry for AI agents to collaborate as a team, with a live dashboard for human oversight.

Provides a durable, Obsidian-compatible knowledge base for agents using markdown notes and wikilinks. Enables agents to store, retrieve, and interlink knowledge persistently, with tools for writing, searching, and managing a graph of notes.

Enables AI agents to retrieve Git-backed project context, handoff packages, progress, and next steps, and to append validated progress events with matching project-record commits. This supports visible agent progress, resumable work, and safe handoffs while keeping high-risk actions outside the MCP surface.

Enables agents to read Git-backed project context and append audited, idempotent progress events through scoped, short-lived credentials, supporting resumable handoffs and visible project state.

Enables AI agents to read Git-backed project context and append validated, audited, idempotent progress events through short-lived, scoped credentials, supporting resumable work and safer handoffs.

Enables agents to query across all their memory stores (brain, team, reading, code) in one call, returning a token-budgeted, ranked briefing with results interleaved from each source.

A robust MCP server that transforms OneNote notebooks into an AI-accessible knowledge base for Gemini Spark, enabling natural language queries to list, read, and search notes via Microsoft Graph API.

A Model Context Protocol server that provides AI assistants with structured access to your Logseq knowledge graph, enabling retrieval, searching, analysis, and creation of content within your personal knowledge base.