"Information about Java programming language or Java-related topics" matching MCP servers:

Generate and render PlantUML diagrams from natural language. A pure Node.js MCP server that creates, validates and explains UML diagrams (sequence, class, activity, state, component and more) as SVG — with zero Java and zero external server. Tools: render_diagram, check_syntax, explain_diagram. Just run npx @plantuml/mcp-js.

Enables inspection and analysis of project structures to detect languages, frameworks, entry points, and dependencies across multiple programming languages including Node.js, Python, PHP, Go, Java, and Rust.

AI-assisted COBOL to modern stack bridge that parses COBOL programs, maps to Python/Java/Go, generates test harnesses, and plans migration for legacy mainframe modernization.

Enables coding agents to natively invoke Java project scaffolding operations such as initializing projects, adding modules, designing systems, and suggesting architectures.

Enables scanning Java/Maven projects to generate TOGAF-standard architecture deliverables (Excel and PPTX) via natural language commands.

Analyzes Java Spring Boot Maven repositories by scanning source code, building a dependency graph, and exposing tools for architecture overview, change impact, dependency cycles, rule validation, and refactoring targets so LLMs can answer architecture questions without rereading the codebase.

Multi-stack scaffolding engine for generating production-ready module structures (NestJS, Java Spring, Python FastAPI) directly from an MCP-compatible AI client, reducing token usage for boilerplate code.

Generate UML class diagrams, sequence diagrams, flowcharts, and component diagrams from any Java or Spring Boot repository via CLI or directly inside Claude Desktop.

Enables analysis of Java call graphs in Axelor projects via ASM bytecode analysis, supporting dependency discovery, change impact analysis, and call chain tracing through SQLite queries.

Mneme exposes a governed Decision Index so MCP clients can propose candidate architectural decisions and query decision state, applicability, and traceability. Proposals remain non-authoritative: accepting or activating protections stays under explicit human control.

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 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.

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.

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.

Generates professional architecture diagrams from natural language descriptions using template-driven prompts and swappable AI image providers.

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.

MCP server for comprehensive code analysis, navigation, and quality assessment across 25+ programming languages.