"Slack 메시지에서 태그된 항목 조회 및 요약 방법" matching MCP servers:

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

Enables AI assistants to generate and validate 30+ UML and other diagram types (Mermaid, D2, Graphviz, BPMN, C4, and more) through the Model Context Protocol, returning rendered SVG, PNG, PDF, JPEG, or base64 output via Kroki, PlantUML, and Mermaid backends.

An MCP server that recommends tech stacks for your project. Describe what you're building and get a recommended stack with reasoning, tradeoffs, and what each layer should use.

Provides UI architecture analysis for converting Figma designs or web pages into reusable UI code blocks with effort estimation in CSV format.

AI-powered codebase health analysis — detects dead code, circular dependencies, coupling issues, and architectural drift. 6 MCP tools for Claude Desktop, Cursor, Windsurf, and Slack.

MCP server that enables AI assistants to create, parse, render, and validate Draw.io diagrams programmatically.

Enables planning AWS architectures, reviewing plans, and provisioning resources after administrative approval with durable execution tracking.

Enables AI agents to record and retrieve architectural decision records, check precedent before making decisions, and avoid repeating past mistakes or contradicting settled choices.

Architecture-index MCP tools for kernelee apps that combine static analysis and runtime wiring into a JSON index, enabling coding agents to query control flow and dependencies via the Model Context Protocol.

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.

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.

Cross-stack MCP server that scans frontend and backend code together, detects mismatches like phantom calls, dead endpoints, and field drift, and provides a full-stack health audit.

Provides a semantic understanding of your codebase by parsing with tree-sitter and building a graph of symbols and dependencies. Enables AI assistants to navigate code, analyze changes, and discover architecture using 18 tools with minimal context overhead.

Enforces a rigorous spec-driven engineering workflow for AI coding assistants, requiring requirements, design, and task planning before any code is written.

This MCP server enables AI models to analyze local Python codebases using abstract syntax trees, providing tools for file structure analysis, symbol search, import graphing, docstring auditing, and refactoring prompts without loading entire source files into context.