"Context Memory Systems and Techniques" matching MCP servers:

Provides systems thinking models as composable analysis lenses for architecture, infrastructure, DevOps, incident analysis, and technical decision-making.

Static codebase analysis as MCP tools — give AI coding agents a map of your repo instead of letting them burn half their tokens rediscovering it file by file.

A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.

An MCP server for managing Domain-Driven Design models using Context Mapper's CML, with tools to create, update, and visualize bounded contexts, aggregates, and relationships.

Local-first MCP server that gives AI assistants codebase intelligence—code graph, drift analysis, vulnerability attribution, and version-correct library docs—all from the user's machine.

A deterministic, read-only MCP server that lets coding agents search Git-backed architecture decision records and retrieve decisions governing specific files. It includes rejected and superseded decisions, with no model, network, or write access.

Enables rendering, validation, formatting, and inspection of D2 diagrams via the Model Context Protocol, using WASM for rendering without requiring the d2 binary.

A Model Context Protocol server that gives an LLM a structured visual layer to describe what exists and get back a clean, precise SVG diagram instead of ASCII art.

Model Context Protocol (MCP) server for @imqueue — lets AI coding agents (Claude Code, Cursor and others) search the docs, scaffold typed services & clients and use @imqueue/cli live.

Generates UML and other diagrams (30+ types) via the Model Context Protocol, supporting multiple output formats and deployment options.

AI-native architecture diagramming for MCP clients (Claude Code, Cursor, Windsurf). The calling agent authors the graph (nodes, groups, edges); Flowgraf validates it, lays it out deterministically with ELK, renders an SVG + Mermaid, and returns a link to a live, editable canvas you can refine by chat, drag, or one-click AI design review. Agent-authored ops — no API key and no LLM cost to the calle

Generates engineer-grade SaaS specifications — design systems, database schemas, and RLS policies for AI build tools like Lovable, Cursor, and Claude Code. 4 tools, TypeScript, MIT licensed.

Enables AI coding agents to discover, convert, and synchronize Draw.io C4 and UML diagrams into Mermaid Markdown, providing architectural context and guardrails during chat sessions.

Provides enterprise architecture knowledge tools that ground agents in curated patterns, enabling translation of messy enterprise asks into realistic architecture recommendations, platform selection, risk checks, and brand context retrieval.

Enables AI assistants to directly control Autodesk 3ds Max through natural language to create models, set materials, adjust lighting, and automate animations. It bridges the Model Context Protocol with 3ds Max via a TCP socket to execute Python and MAXScript commands.

Enables AI hosts to generate editable online diagrams (flowcharts, mindmaps, UML, and more) from natural language or Mermaid, using a ProcessOn account via the Model Context Protocol.

Enables AI assistants to generate and validate 30+ UML and other diagram types (Mermaid, D2, Graphviz, BPMN, C4, and more) through the Model Context Protocol, returning rendered SVG, PNG, PDF, JPEG, or base64 output via Kroki, PlantUML, and Mermaid backends.

An MCP server that recommends tech stacks for your project. Describe what you're building and get a recommended stack with reasoning, tradeoffs, and what each layer should use.

Enables analysis of any GitHub repository to get architecture, file roles, execution flows, system design Q\&A, and structured agent context. Works with MCP-compatible clients like Claude Desktop, Cursor, and Windsurf.

A cross-project knowledge graph for MCP that maps features across mobile, backend, and admin codebases, giving AI agents full-stack context.