"Web Page Content" matching MCP servers:

An MCP server that lets AI agents drive FigJam boards through a local Figma plugin, bypassing API rate limits with 19 tools for reading, writing, and editing FigJam content.

An MCP server that enables LLMs to create and open diagrams in draw.io using XML, Mermaid, or CSV formats, by building a URL with compressed content and opening it in the browser.

Validates and parses Draw.io XML syntax, enabling analysis and verification of diagram structures, especially for AI-generated content.

Enables multi-dimensional architecture review of technical solutions with web intelligence and knowledge base auto-sedimentation, acting as a logical security gateway for Claude Code.

Enables an AI assistant to load, query and edit a YAML or JSON graph file, with tools for adding, editing and removing nodes and edges, managing reusable styles and graph-level properties, and saving changes. Every change is written to the file immediately, so the same graph can be viewed and edited live in the companion web app.

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

Provides comprehensive design principles and best practices to help LLMs generate modern, accessible web pages through guidance on layouts, colors, and typography. It enables users to review design approaches and access expert recommendations for responsive design, component structure, and current industry trends.

Enables an AI assistant to author algorithm storyboards, workflow maps, and UI styling through MCP, serving a local web app for visualizing and reviewing them.

Automatically generates mind map images from text content using Coze API workflow. Returns accessible image links that can be used in MCP-compatible tools like CodeBuddy, Cursor, and Qoder.

Enables users to create, validate, view, and export system stock-and-flow maps from a versioned JSON model through MCP-capable hosts over local stdio or hosted Streamable HTTP. Supports causal-loop and nested-level mapping while keeping submitted model content with the caller and requiring no LLM provider key.

An MCP server for managing Domain-Driven Design models using Context Mapper's CML, with tools to create, update, and visualize bounded contexts, aggregates, and relationships.

Enables creation and management of state/decision graphs using NetworkX, supporting directed graphs with conditional branches, edge ordering, path finding, and visualization through PNG exports or interactive web interface.

Local-first MCP server that gives AI assistants codebase intelligence—code graph, drift analysis, vulnerability attribution, and version-correct library docs—all from the user's machine.

Enables AI agents to visualize tasks on a collaborative Sketchbord whiteboard by composing content into diagrams, editing them incrementally, and reading back the board including user-drawn additions.

Static codebase analysis as MCP tools — give AI coding agents a map of your repo instead of letting them burn half their tokens rediscovering it file by file.

Turns structured JSON content into resolved, audited infographic plans and renders them as SVG or executable Figma Plugin API scripts, exposing theme packs, grammars, planning, auditing and export tools over stdio. Also lets hosts inspect theme provenance and layout decisions so different tools produce identical frames.

Enables an agent to inspect and replace .petrinet source code open in a live browser page of the Velocitron simulator.

A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.

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.

Enables AI assistants to create, edit, and export flowcharts through a local web-based editor with visual drag-and-drop, real-time sync, and 14 MCP tools for full node/edge CRUD.

Enables rendering of diagrams (sequence, flowchart, ER, cloud architecture, etc.) using the Eraser.io API with customizable themes, backgrounds, and scaling options. Returns image URLs or base64-encoded content with built-in icon validation.

Enables agents to publish Mermaid diagrams by submitting them through a serve_design tool, which stores them in a companion web service and returns a shareable URL where the rendered diagram can be viewed.

An automated MCP server for Google Stitch that enables AI-driven UI design generation, accessibility audits, and design system management. It streamlines workflows for creating responsive screens, extracting design tokens, and maintaining visual consistency across professional web projects.

Enables agents to generate a JSON sitemap of how a path through a TypeScript/JavaScript codebase runs, capturing the summary, areas, every step with its file, lines, description, calls and data, and the links between services. The resulting document can be opened in a viewer that draws the same map as a single page, and rebuilt in CI to keep system documentation current.

Provides AI assistants with access to a production-ready design system including Tailwind CSS component patterns, style guides (colors, typography, spacing), and Web Components specifications for consistent UI development.

MCP server for the Product Framework that enables agents to author and verify a What/How graph, with a live web view for visualizing the domain model, event flows, and system maps.

Enables AI agents to create and edit Whimsical diagrams and documents, including flowcharts, mind maps, sequence diagrams, and wireframes, and to search and read workspace content.

Enables Claude to analyze any public GitHub repository through typed, focused tools that surface metadata, README content, file structure, detected tech stack, and Mermaid architecture diagrams. It lets users explore repo purpose, structure, and dependencies via natural language without manual browsing.

Gives AI agents structured tools to maintain a living requirements traceability graph — requirements to user stories, cucumber scenarios, screens and phase results — so coverage and its trend across releases can be queried on demand and explored through a web dashboard.

Enables agents to create display-quality visual content across HTML, Mermaid, pseudocode, text trees, and diffs, with automatic layout repair and rendering verification to prevent overlaps, overflow, and connection conflicts.