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"CSS Modules" matching MCP servers:

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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.
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    MIT
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    Eyes for AI coding agents: deterministic MCP server that tells the LLM which CSS rule wins, in which file, on which line — and why. Cascade verdicts, blast radius, interaction timelines, pixel-perfect audits.
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    Apache 2.0
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    A companion MCP server that makes CSS legible to AI agents by resolving cascade, tracing properties, and explaining what applies and why before an agent modifies styles.
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    MIT
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    Security MCP server with 300+ rules for AI-generated code. Scans Next.js, Supabase, Clerk, Stripe, Prisma, Hono, GraphQL and 20+ modules. Zero config, runs locally.
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    Apache 2.0
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    A local-first MCP preflight system to help AI inspect Python repositories before generating code, enabling reuse, extend, or create decisions. Currently in early development with only foundational modules implemented.
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    MIT
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    Provides Python refactoring capabilities via the Rope library, enabling AI agents to perform safe, project-wide code transformations such as renaming symbols, moving modules, and extracting methods.
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    MIT
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    Provides an MCP interface for Agda's JSON interaction protocol, enabling type checking and interactive editing of Agda modules with support for workspace management and non-blocking operations.
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    MIT
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    Enables AI assistants to extract and document Mendix Studio Pro modules by interrogating local .mpr files. Generates comprehensive JSON documentation of domain models, pages, microflows, and enumerations without sending data to the cloud.
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    Enables AI to directly read and analyze Lanhu design drafts and requirement documents to generate HTML, CSS, and structural analyses. It allows users to extract design slices and process prototype pages directly within AI clients.
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    MIT
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    Enables intelligent code analysis and search across repositories using the CodeRank algorithm (inspired by PageRank) to identify critical modules, trace dependencies, find code hotspots, and perform context-aware keyword searches with importance-ranked results.
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    Apache 2.0
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    Enables AI agents to read and understand local Mendix project structure and logic by connecting directly to the .mpr file via MCP. Allows querying microflows, entities, attributes, and modules in read-only mode without requiring cloud access.
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    Finds the closest Tailwind CSS palette colors to any given CSS color value. Supports multiple color spaces and customizable result filtering to help match designs to Tailwind's color system.
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    An LLM-optimized server for reverse-engineering and navigating minified JavaScript bundles by splitting them into searchable, individual modules. It enables LLMs to analyze code architecture, extract specific symbols, and perform semantic searches while efficiently managing context window usage.
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    MIT
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    Provides static code analysis for enterprise-scale Haskell codebases with 40+ comprehensive tools for analyzing modules, functions, types, classes, imports, and architectural dependencies through real-time queries.
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