"Files formatted in JSON" matching MCP servers:

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.

13 AI-powered UML diagram tools for coding agents. Generates class, sequence, state, ER, threat model, deployment and architecture diagrams in Mermaid syntax. Works in Claude Code, Cursor and Windsurf.

Enables generating, reading, auditing, and previewing BPMN 2.0 diagrams for Bizagi Modeler, with an automatic layout engine that produces files Bizagi can open, plus optional control of the Bizagi Modeler desktop app on Windows.

Enables auto-configuration and validation of React components against a glassmorphic design token system using an AST correction engine. It supports managing UI presets, enforcing design uniformity in Monaco IDE, and exporting tokens as CSS, Tailwind, or JavaScript variables.

MCP server for AI Diagram Maker — generate software engineering diagrams from natural language, code, ASCII diagram, images, or Mermaid. Inline diagram rendering using MCP apps UI and diagram URL in responses. Works with Cursor, Claude Desktop, Claude Code, and any MCP-compatible AI.

Enables AI coding agents to retrieve mode and stage guidance so they can lead users through a 9-stage design workflow, including questions, required artifacts, exit criteria, dependencies, pitfalls, rollback impact, and environment requirements. It helps non-designers turn ideas into buildable HTML/WPF design specs while keeping the human in control of confirmation and revisions.

An MCP server that generates diagrams from Mermaid code in multiple formats (PNG, PDF, SVG).

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.

MCP server that provides a live, terminal-native layered dependency map for AI coding assistants, with tools to track and visualize development progress in real time.

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.

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 two coding agents to deliberate on a plan in a shared room, taking turns to argue and converge until the lead writes a spec file and closes the room.

Converts Markdown files into professional diagrams such as flowcharts, mind maps, and architecture diagrams using Mermaid.js and custom SVG renderers. It enables users to list, render, and export visualizations in multiple formats including SVG, PNG, and PDF.

Lets agents construct Mermaid diagrams from structured JSON IR and render them as PNG or SVG.

Enables agents to read, edit, validate, and simulate Poietic Stock and Flow designs by driving the poietic CLI, allowing collaborative work with humans in Poietic Playground.

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 assistants to generate professional UML diagrams (class, use case, activity, sequence) from natural language descriptions, producing editable .drawio files compatible with diagrams.net.

An MCP server that enables LLMs to create and open diagrams in draw.io using XML, Mermaid, or CSV formats, by building a URL with compressed content and opening it in the browser.

Enables creating CORAS risk and threat diagrams from natural-language descriptions, producing .dgx files that the SINTEF Threat Modelling Tool opens natively, including layout, validation, and rendering.

Tracks architectural decisions with testable predictions, outcome validation, and in-band feedback nudges to close decision loops for AI agents and teams.