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    An MCP server that builds a local webpage to view AI plans, reports, and diagrams, so the AI only needs to send a URL pointer instead of long text. It provides three tools (view_plan, view_report, view_diagram) and tracks project changes for diff viewing.
    3
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    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.
    14
    MIT
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    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
    3
    74
    MIT
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    An MCP server that maps any repository into architecture diagrams, providing tools for scanning code facts, building import graphs, and generating Mermaid/DOT diagrams, plus architecture rule enforcement for CI.
    5
    303
    MIT
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    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.
    19
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    1
    MIT
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    An MCP server that generates standalone SVG architecture diagrams from text descriptions, running entirely on your machine with no dependencies or network access.
    8
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    An MCP server designed for React Native and Expo development that provides specialized tools for project scaffolding, architectural best practices, and troubleshooting. It enables AI assistants to guide users through setup, navigation configuration, and CI/CD processes using modern stacks like NativeWind and Zustand.
    13
    4
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    An MCP server for Spec-Driven Development that transforms natural language ideas and meeting transcripts into structured, production-grade specifications using EARS notation. It automates a 7-phase pipeline to generate project artifacts like requirements, architecture designs, and task lists directly to disk.
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    MIT
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    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.
    3
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    ISC