"C# Azure .NET development assistance" matching MCP servers:

MCP server for Next2D application development that generates code following MVVM + Clean Architecture + Atomic Design patterns, validates architecture, and provides API references.

Enables creation of Azure architecture diagrams from natural language descriptions, generating PNG images automatically.

A remotely-hosted MCP server that generates Azure architecture diagrams from agent-generated topology descriptions, with safe execution and consistent styling.

Analyzes C++ source code to automatically generate UML class diagrams in PlantUML format, extracting classes, inheritance relationships, and member visibility from directories or file contents.

An MCP server that generates professional infrastructure diagrams using the Python diagrams DSL, with first-class Azure support and GitHub Copilot integration for natural language diagram generation.

Enables AI to analyze, query, and manage a graph-based representation of software architecture for impact analysis, dependency tracking, and design.

MCP server that analyzes codebases to provide dependency graphs, impact analysis, and file insights across 15+ programming languages, enabling AI assistants to understand project structure and navigate code efficiently.

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

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.

A comment-based spec-driven development MCP server that facilitates workflows from requirements creation to implementation via AI, using @spec-impl markers.

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.

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.

Local code-graph engine for AI coding workflows: static analysis of Rust, Cangjie, ArkTS, TypeScript/JavaScript, C/C++, Python, and Shell projects into symbols, imports, call graphs, and quality gates, with pre-commit change review and impact analysis. Read-only by design — never executes build scripts or uploads code.

An MCP server designed for React Native and Expo development that provides specialized tools for project scaffolding, architectural best practices, and troubleshooting. It enables AI assistants to guide users through setup, navigation configuration, and CI/CD processes using modern stacks like NativeWind and Zustand.

MCP server exposing 3100+ cloud technology icons (AWS, Azure, GCP, Microsoft, CNCF, Devicon) with 3-tier search and multiple output formats for integration into presentations and architecture diagrams.

Generates, reviews, and imports cloud architecture diagrams for AWS, Azure, and Google Cloud from editable specs, Terraform, or live Google Cloud inventory, rendering them as PNG, SVG, PDF, or JPG.

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

An MCP server for Spec-Driven Development that transforms natural language ideas and meeting transcripts into structured, production-grade specifications using EARS notation. It automates a 7-phase pipeline to generate project artifacts like requirements, architecture designs, and task lists directly to disk.

An MCP server for evaluating C# project architecture quality using the Modularity Maturity Index (MMI) framework. It provides tools for detecting layering violations, circular dependencies, and encapsulation issues with interactive D3.js visualizations.

An MCP server implementing Spec-Driven Development workflows for AI-agent CLIs and IDEs like Claude Code and Cursor, enabling spec-first development with automated workflow guidance and quality checks.

Professional AI-powered architecture diagram generator with multi-cloud support and MCP server integration. Generates beautiful, accurate diagrams with provider-specific icons for AWS, Azure, GCP, Kubernetes, and more.

Connects AI models to MasterGo design tools, enabling retrieval of DSL data, component documentation, and metadata from MasterGo design files for structured component development workflows.

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.

A semantic map of your .NET solution for AI coding agents. Analyzes a solution with Roslyn into a queryable code graph exposing 11 read-only tools (find_symbol, impact_analysis, find_implementations, etc.) over MCP. 100% local, no telemetry, MIT licensed.

An MCP server that adds engineering discipline to AI-assisted development, enforcing evidence-gated TDD, security review, backup strategy, and deployment generation to turn AI-generated code into production-ready software.

Enables AI assistants to discover, document, and generate React code using the pastel-themed Baby Design UI component library. It provides tools for retrieving design tokens and component specifications to streamline the development of user interfaces.

A demonstration MCP server for exploring MCP prompts with 10 reusable development prompts.

Enables AI assistants to explore and visualize a .NET codebase's architecture, generating dependency diagrams, Entity Relationship Diagrams from EF Core contexts or model snapshots, class hierarchy diagrams, and solution health metrics through natural language.

Enables visualization of Terraform plan changes as interactive cloud architecture diagrams with official AWS, Azure, and GCP icons, showing resource dependencies and changes.

Enables AI coding agents to declare and maintain a live, layered dependency map during bottom-up development, exposing eight mmap_* tools that ghost the intended design first and then light nodes up as they are built and verified in a terminal pane beside the conversation.