"A tool or resource for analyzing skills" matching MCP servers:

An MCP server that helps transfer C4 diagrams from DrawIO and PlantUML into a DocHub architecture repository.

Automatically generates Architecture Decision Records (ADRs) from Claude Code conversations using Claude Opus. Features AI quality review, duplicate detection, and dependency graph.

An MCP server that equips AI assistants with specialized, opinionated development playbooks for building microservices, frontends, databases, and DevOps pipelines. It integrates with clients like VS Code Copilot, Claude Desktop, and Cursor to provide battle-tested expertise.

Enables coding agents to draw and edit Excalidraw diagrams on localhost by rendering Mermaid source into a live-updating canvas synced through a context file, with support for reusable icon libraries. No API key is required.

Translates natural language commands into structured geometric operations for a simulated CAD engine using LLMs. It enables users to create shapes, modify dimensions, and perform 3D operations like extrusion through the Model Context Protocol.

Provides UI architecture analysis for converting Figma designs or web pages into reusable UI code blocks with effort estimation in CSV format.

A MCP server for SailfishOS application development, creates new projects and add features following guidelines, performs build

Enables interactive MATLAB development by allowing users to execute scripts and specific code sections, manage workspace variables, and capture generated plots. It integrates with the MATLAB Python Engine to provide persistent execution context for MCP-compatible clients.

Enforces a rigorous spec-driven engineering workflow for AI coding assistants, requiring requirements, design, and task planning before any code is written.

MCP server for creating and editing diagrams using draw.io. Allows generating diagrams from Mermaid or XML, searching shapes, and opening them in draw.io for export.

Cloud infrastructure provisioning via natural language. Enables deploying production-ready infrastructure on AWS, GCP, Azure, or Oracle with a single prompt.

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

Enables creating, managing, analyzing, and visualizing knowledge graphs with support for multiple graph types (topology, timelines, changelogs, requirements, knowledge bases, ontologies) including node/edge management and resource association.

Connects Claude directly to Godot 4 projects to read and manipulate scenes, nodes, scripts, and assets within the editor. It enables real-time scene tree management, script editing, and resource configuration through a local Node.js bridge and GDScript plugin.

Renders diagram-as-code to PNG files on your disk, and also provides text-art previews and syntax validation, all locally without a browser or network.

Gives AI agents instant access to software architecture, dependencies, and impact analysis through pre-computed sgraph models, replacing dozens of grep/read cycles with a single tool call.

MCP server for generating and editing architecture diagrams from natural language or code, supporting formats like Terraform, docker-compose, Kubernetes, SQL, Mermaid, and PlantUML.

diagramzu-mcp lets an AI client (Claude, Cursor, ChatGPT, etc.) author Mermaid diagrams in a shared workspace that a team can read, comment on, embed, and present at a clean URL — rather than just generating a one-off diagram. Hosted endpoint https://mcp.diagramzu.ai/mcp or local npx -y @diagramzu/mcp. Also listed in the official MCP registry as ai.diagramzu/mcp.

An MCP server that generates standalone SVG architecture diagrams from text descriptions, running entirely on your machine with no dependencies or network access.

Enables AI coding agents to make evidence-aware design-pattern decisions by analyzing cases, comparing options, detecting misuse, stress-testing assumptions, planning adoption, writing ADRs, and generating evidence plans with transparent scoring.