"Analyzing Python Modules, Docker Files, and Generating Mock Data or Outputs" matching MCP servers:

Exposes the 23 Gang of Four design patterns to AI coding agents for generation, canonical examples, AST-based detection, validation, and anti-pattern refactoring in Python codebases.

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.

Exposes read-only Huly task intake, architecture guidance, UX patterns, and static validators through eight MCP tools, letting host agents drive approval-gated Flutter and Frappe delivery workflows. It can also be inspected directly (e.g. via the MCP inspector) so users can exercise the tools without an agent.

Enables users to create, validate, view, and export system stock-and-flow maps from a versioned JSON model through MCP-capable hosts over local stdio or hosted Streamable HTTP. Supports causal-loop and nested-level mapping while keeping submitted model content with the caller and requiring no LLM provider key.

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

An MCP server that designs software projects by drafting design documents with an LLM, refining them, answering questions, and creating projects via the AI2DEV API.

MCP server for generating and validating FrameIO modules and plugins, with code generation, validation, examples, and step-by-step guidance.

Provides architecture design expertise to AI coding agents, analyzing requirements, selecting architecture patterns, generating concrete designs, and evaluating quality attributes.

Enables generating GCP architecture, sequence, flow, and class diagrams using natural language or Python code via the diagrams library.

Enables AI-powered threat modeling with tools for creating threat models, analyzing security threats, generating security controls, and validating architecture against best practices.

This enterprise MCP server template provides a production-ready, architecture-first foundation for building MCP servers in Python, with capability registry, dependency injection, and Docker support.

Generates GCP architecture diagrams, sequence diagrams, flow charts, and class diagrams using Python diagrams DSL via MCP.

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.

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.

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

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.

Blueprint is an MCP server that enables AI assistants to create C4 software architecture diagrams from structured data.

Enables AI assistants to interact with Autodesk Civil 3D, allowing them to retrieve project data, create/modify/delete drawing elements, and execute code to automate Civil 3D operations.

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.

Enables MCP-capable coding agents to access SkeletIQ architecture releases, including reading designs and build orders, generating and critiquing architectures, and reporting build progress and drift.

🐍 🏠 - Code graph for AI agents over MCP: resolves Python and TypeScript calls to fully qualified names and reports the share it could not resolve instead of guessing. Multi-hop impact analysis, authz reachability audit, architectural drift against declared patterns. Local, SQLite. uvx codegraph-brain

An MCP server that maps any repository into architecture diagrams, providing tools for scanning code facts, building import graphs, and generating Mermaid/DOT diagrams, plus architecture rule enforcement for CI.

Lets Claude Code, Cursor, VS Code and other AI agents draw and edit draw.io diagrams from your project's files, on a live browser canvas you can also edit by hand. Includes version history, undo, cloud shape libraries (AWS, Azure, GCP) and PNG or SVG export.

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.

Official draw.io MCP server: create diagrams from Mermaid, XML or CSV, shown inline in Claude, ChatGPT, VS Code and Cursor or opened in the draw.io editor. Searches 10,000+ shapes.

Distills Claude Code's 310K-line source into 32 queryable modules via MCP, enabling AI agents to learn and reuse Claude Code's architectural patterns through natural language.

Lets AI agents draw professional cloud architecture diagrams with the official AWS, Azure and Google Cloud icons, from a plain description, a JSON graph or Terraform code.

Enables AI assistants to analyze code repositories and generate self-contained interactive HTML5/SVG architecture diagrams from project structure or existing Mermaid diagrams, fully air-gapped with no external dependencies.