"Creating a Framework Design in Draw.io" matching MCP servers:

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

MCP server bridging ChatGPT-compatible clients to the Design Flow System, enabling project session management, draft import, round locking, and read-only access to design state and traceability.

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.

Provides source-backed design context, route card validation, contract generation, critique and verification reports, evidence packages, Penpot change plans, and anti-repeat checks for design workflows. Does not directly mutate Penpot, but consumes read-only Penpot snapshots.

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.

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

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.

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

Design contract layer for AI agents. Scans Figma, code, Storybook, and token files, reconciles conflicts, and serves a single machine-readable source of truth so every agent gets the same authoritative design rules before it builds. Local-first.

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

Enables AI agents to act as a state-driven tutoring engine over MCP, with server-side mastery estimation, prerequisite checks, evidence-based assessment, and FSRS spaced review scheduling.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Persistent decision memory and contradiction detection for AI coding agents. Enforces architectural consistency across sessions — the agent cannot code until it loads prior decisions. Human resolves conflicts on a dashboard or in chat.

Project management and session memory for AI agents that provides 13 tools for tracking projects, tasks, and context during AI coding sessions.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

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 agents to search a local SQLite knowledge base before fetching, save fetched documents with deduplication, and retrieve full text with metadata, preventing redundant fetches and fabrication.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.