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"Enforcing Apple Brand Style Guidelines for Team Communication Drafts Without AI Style Learning" matching MCP servers:

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    A tiny local MCP server that learns a user's conversational style, catchphrases, dialect markers, emoji habits, tone preferences, and concrete collaboration preferences without storing private memories.
    15
    1
    MIT
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    Provides deterministic design style recommendations and structured tokens for AI content generation, with 30 curated styles including color palettes, typography, and visual directives.
    2
    391
    MIT
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    Provides a catalog of curated author writing styles and tools to blend or analyze them across eight dimensions for text and image prompt generation. It enables users to apply structured literary patterns through deterministic style modeling and coordinate-based interpolation.
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    A TypeScript ESM MCP server exposing 20 public instruction tools and 7 utility tools, backed by 102 internal skills across 18 domain families — from requirements discovery and code quality through governance, resilience, and physics-inspired analysis.
    665
    6
    MIT
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    A memory-backed brand generation runtime for agent-led creative iteration. Enables AI agents to plan, generate, review, and improve brand materials with persistent brand memory and structured workflows.
    MIT
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    A server that automatically reviews code style and suggests improvements, with special focus on Flutter/Dart projects and general programming styles.
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    MCP server that encapsulates Apple Mail on macOS via AppleScript, enabling invoice PDF search, attachment saving, and message flagging through natural language.
    5
    MIT
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    Provides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.
    32
    Apache 2.0
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    Enforces brand writing guidelines in Claude Code by analyzing Markdown files for violations and offering on-demand readability analysis and automated term fixes through MCP tools.
    61
    MIT
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    A minimal FastAPI implementation that mimics Model Context Protocol functionality with JSON-RPC 2.0 support. Provides basic tools like echo and text transformation through both REST and RPC endpoints for testing MCP-style interactions.