"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.

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.

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.

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

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 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.

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.

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.

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.

Local-first MCP server for storing and managing project architecture, modules, data flow, and usage examples with complete privacy.

Enables creation of infrastructure and architecture diagrams as code using the Python diagrams library, supporting 15+ cloud providers, 500+ node types, custom icons, and flowcharts with multiple output formats.

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.