"Design Architecture of Ontology-based AI-PHM Using Life Acceleration Algorithms" matching MCP servers:

Enables conversational low cycle fatigue analysis with standardized reduction, material constants fitting, and life predictions from strain-controlled test data.

Enables local, deterministic semantic operations on OWL/RDF ontologies, including Description Logic reasoning, SPARQL querying, TBox/ABox mutation, and interactive graph visualization via an MCP interface.

An unofficial MCP server for generating infographics and visuals from text using the Napkin AI API, enabling AI assistants to create professional diagrams, mindmaps, and charts.

Enables AI assistants to generate and render individual and composite Human Design charts, explore authorized chart and library data, and manage account usage and Website Builder projects with secure OAuth and entitlement filtering.

MCP server for AI-driven CAD operations, enabling parametric modeling, build, inspection, measurement, rendering, and diff comparison with STEP, DXF, and GLB export.

Enables AI-native statistical analysis and reproducible research workflows through MCP, including natural language planning, protocol-based analysis, Python/R cross-validation, and publication-ready figure generation.

MCP server for A-share stock technical analysis and AI prediction, enabling LLM-based interaction to analyze stocks.

Enables controlling Power BI with AI through natural language commands for creating DAX measures, explaining metrics, suggesting KPIs, and organizing models.

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.

Enables AI hosts to generate and open Mermaid diagram links in Markdown files, providing tools for syncing, listing, and injecting diagrams via the Mermaid.ai editor.

Enables AI clients to generate over 26 chart types via MCP, automatically starting a private rendering service.

Enables transforming grounded AI answers into interactive information views such as comparisons, plans, checklists, timelines, and decision views, while preserving a plain-language fallback.

Enables creating surveys, validating questions, collecting responses, and generating analysis reports.

Enables conversational spatial SQL queries in plain English, turning natural language questions into validated PostGIS operations and rendering results as GeoJSON on an interactive map.

Render hierarchical system diagrams as PNGs from YAML definitions using a four-level ontology of networks, factories, machines, and nodes.

Enables interactive design, execution, and analysis of SUMO traffic simulations through natural language, providing tools for scenario generation, policy experimentation, result analysis, and visualization.

Generate 25+ types of hand-drawn Excalidraw diagrams - flowcharts, sequence, architecture, mind maps, ER, UML, kanban, bar/line/radar charts - from structured input with Sugiyama auto-layout and CJK support. Reads and modifies .excalidraw files, exports SVG. Python, local, no API keys required.

MCP server that provides F1 telemetry analysis, tyre degradation modeling, and pit strategy recommendations, delivering race engineer-style calls grounded in real data. It exposes tools for degradation fits, fuel correction, and strategy reasoning via LangGraph.

AI-powered data visualization MCP server that generates publication-ready charts from natural language descriptions while keeping data local.

Opens, inspects, and renders DXF/CAD drawings for AI agents: describe_dxf returns structured facts (layers with actual drawn colors, units, bounds, text content) and render_dxf returns PNG images. On MCP Apps hosts like ChatGPT and Claude, view_dxf opens an interactive in-chat viewer with pan, zoom, and layer toggles.