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    GoHighLevel MCP server for Claude. 212 tools across 43 modules, including the only programmatic GHL workflow builder (private API, reverse-engineered), funnel + page editor, form builder, pipeline builder, pre-deploy validator, multi-sub-account switching, bulk operations, and full account export. $97 one-time, lifetime updates.
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    An MCP server that uses Claude 3.5 Sonnet to transform ordinary prompts into structured, professionally engineered instructions for any LLM. It enhances AI interactions by adding context, requirements, and structural clarity to raw user inputs.
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    MIT
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    A proxy server that enables AI assistants to run and interact with command-line applications like Expo through the Model Context Protocol (MCP), capturing logs and allowing keypress forwarding.
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    MIT
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    An MCP server that provides session-based shell command execution and file management with intelligent LLM-powered summarization. It enables users to manage isolated workflows and track progress through automated event logging and session history.
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    Optimizes prompts using meta-prompting techniques through a two-turn approach that first provides optimization guidelines and then refines the prompt for enhanced quality and effectiveness.
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    MIT
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    Allows executing shell commands within a secure Docker container through Claude's MCP interface, providing Kubernetes tools and isolated environment without host Docker daemon access.
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    MIT
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    An MCP server that registers customizable prompt templates as tools for natural language interaction. It supports hot-reloading and easy extension via YAML or JSON files to automate tasks like code review, refactoring, and content summarization.
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    MIT
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    A secure Model Context Protocol server that allows AI assistants to execute terminal commands with controlled directory access and command permissions. It features a robust security architecture including whitelisting, session IDs, and categorized command levels to ensure safe system interaction.
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    MIT
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    Automatically analyzes and optimizes AI prompts by calculating clarity scores, detecting risks, asking clarifying questions, and adding domain-specific requirements to improve AI interaction quality.
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