"Information about Docker containerization technology" matching MCP servers:

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

Extracts deterministic architecture maps from codebases for AI agents, enabling queries about blast radius, routes, security findings, and production readiness without sending code anywhere.

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.

Enables AI assistants to understand and analyze Spring Boot codebases through static analysis, allowing natural language queries about endpoints, classes, dependencies, and architecture without running the application.

Enables AI agents to create, validate, and manage buildingSMART IDS (Information Delivery Specification) files with 100% compliance to the IDS 1.0 standard using IfcOpenShell's IfcTester library.

Semantic React/TSX analysis MCP server that saves 70-90% tokens by using AST to retrieve precise component usages, prop flow, and state update information for AI coding assistants.

Enables AI assistants to check server status, inspect Camunda Hub API v2 connection information, and read Hub files by UUID, with planned support for prompt-driven BPMN diagram creation, editing, validation, and visual review.

Indexes a workspace into a graph of files, modules, symbols, SQL tables, API contracts, and CI/container files, then answers questions about it. Enables querying dependencies, affected tests, blast radius of a change, node focus, and diffs between refs, all read-only and within a configurable byte budget.

Enables AI clients and agents to turn a new idea into a structured set of clarifying questions, technology options, upward design breakdowns, reverse reasoning, and risk assessments so users can steer top-level design decisions. It also supports saving insights and building a bidirectional design tree of concepts and their implementations.

A Best Practices Oracle that provides dynamic advice, checklists, and architectural patterns for various software technologies. Helps developers with refactoring, testing guidelines, security checklists, and deployment principles through technology-aware recommendations.

Enables AI agents to query a repository's verified semantic model, including capabilities, ownership, contracts, constraints, impact, health, and verification status, without re-deriving project facts. It exposes read-only tools for inspecting, reasoning about, and verifying the provenance-tracked surface.

Enables managing Cacoo diagrams, folders, organizations, and account information through MCP tools over HTTP, with OAuth-based authentication, email allowlisting, and support for multiple Cacoo accounts.

Enables AI coding agents to manage domain-model projects and generate code for multiple technology stacks directly from a conversation.

Retrieves architectural information from ArchiMate models, enabling AI coding assistants to access architectural context during the software development lifecycle. Supports search and retrieval of views and elements in markdown, JSON, or YAML.

Generates technical documentation and diagrams (C4, UML, flowcharts, Gantt, etc.) using MCP protocol, with Docker-based tooling and optional AI image generation via DALL-E 3.

Builds a dependency and topology graph of a Kubernetes/FluxCD GitOps repository via static analysis, enabling AI agents to reason about service relationships without cluster access.

Enables AI assistants to programmatically create and manage information flow diagrams — information items and information flows in StarUML.

Enables users to start from a seed host, traverse CDP/LLDP neighbor evidence and device information over SSH, and produce an editable Draw.io topology diagram plus supporting evidence for engineer review. It supports explicit host/CIDR scoping, bounded depth and device counts, strict host-key checks, and a read-only command policy, with an offline simulated demo.

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.

Enables AI coding assistants to turn architectural and conceptual descriptions into fully laid-out, hand-drawn Excalidraw diagrams across 25 diagram types, with automatic technology-aware styling, natural-language revisions, and SVG/PNG export. It can also persist systems as a queryable, version-controlled architecture knowledge graph while running entirely offline without API keys.

An intelligent MCP server for the automated design and sizing of aerobic biological wastewater treatment systems using membrane bioreactor (MBR) technology. It leverages ASM2d kinetics to perform mass balance calculations, flowsheet selection, and generate detailed engineering reports.