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    An MCP server that enables AI assistants to control Blender through 108 specialized tools for 3D modeling, animation, and rendering. It provides a secure, thread-safe interface to execute validated operations in Blender using natural language commands.
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    AGPL 3.0
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    An MCP server that enables AI assistants to programmatically create and manage flowcharts, ERDs, and swimlane diagrams. It provides tools for manipulating diagram structures, performing auto-layouts, and exporting to Mermaid or Markdown formats.
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    MIT
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    Provides AI-powered educational tools for teachers including lesson planning, quiz creation, student progress analysis, learning path recommendations, and rubric generation. Enables educators to automate administrative tasks and personalize learning experiences through natural language interactions.
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    MIT
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    Enables AI assistants to present multiple UI component designs in a browser for visual selection, with AI-powered design generation using MiniMax-M2.1 from natural language prompts or manual HTML input.
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    MIT
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    Sends system notifications when AI completes responses in MCP-compatible code editors, with cross-platform support for macOS, Windows, and Linux including customizable titles, messages, icons, and sound effects.
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    A text processing workbench and MCP server that provides 22 tools for cleaning, transforming, and extracting data from text. It enables AI assistants to perform complex operations like regex extraction, log normalization, and encoding through a unified interface.
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    3
    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.
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    Apache 2.0
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    Provides SEO automation with tools for keyword research, SERP analysis, and competitor analysis through Google Ads API integration, enabling AI assistants to access these capabilities via MCP.
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    Drive the Unreal Engine 5.7 editor from any MCP client over a local TCP socket - 105 editor-automation tools (72 native C++ handlers + 33 bridge-side): actors, levels, materials, Blueprints, sequencer, rendering, asset import, editor Python. Native C++ plugin + thin Python bridge, ~50ms round-trips. MIT.
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    MIT
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    Manage Job using MCP: Manage Job, Candidates, Resumes, Salaries all within this one MCP tools It can solve problems like: You have 50 resumes to screen. Your AI assistant can reason about candidates, but it can't: Read PDFs/DOCX — The AI can't open binary files Extract structured data — Copy-pasting loses formatting, metrics, and context Compare at scale — No consistent scoring across candida
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    MIT