This project provides a PowerPoint automation server that integrates with Claude Desktop using the Model Control Protocol (MCP). It allows users to automate tasks in Microsoft PowerPoint, such as creating presentations, adding slides, modifying content, and more. The server leverages the PowerPoint COM API to provide a wide range of automation capabilities, making it a powerful tool for users who need to streamline their PowerPoint workflows.

PyMOL-MCP connects PyMOL to Claude AI using the Model Context Protocol (MCP), allowing Claude to directly control PyMOL through natural language commands. This integration facilitates conversational structural biology, molecular visualization, and analysis. Features include two-way communication, intelligent command parsing, molecular visualization control, structural analysis, and code execution within PyMOL.

The MCP server provides tools for managing Kubernetes clusters, including pod, deployment, and service management. It also offers workload analysis capabilities to monitor and optimize cluster performance. Built with Go, it requires kubectl configuration for seamless integration with Kubernetes environments.

The Coolify MCP Server is designed to integrate with Coolify instances, providing a range of management features such as teams, servers, services, applications, deployments, and private keys. It facilitates operations like starting, stopping, and restarting services, as well as tracking deployments and managing resources. This server is compatible with Coolify version 4.0.0-beta.397 and requires Node.js 18 or higher for installation.

Container-MCP provides a sandboxed environment for safely executing code, running commands, accessing files, and performing web operations requested by large language models. It implements the MCP protocol to expose these capabilities as tools that can be discovered and called by AI systems in a secure manner. The architecture uses a domain-specific manager pattern with multi-layered security to ensure tools execute in isolated environments with appropriate restrictions, protecting the host system from potentially harmful operations.

The Netbird MCP Server is a specialized implementation of the Model Context Protocol (MCP) designed to integrate with Netbird, a networking platform. It provides functionalities such as listing Netbird peers with detailed information, managing groups and policies, and secure token-based authentication. This server is derived from the MCP Server for Grafana and is written in Go, offering a configurable API endpoint for seamless integration.

The Workflows MCP Server enables the creation of reusable and customizable AI workflows by combining prompts and MCP servers. It allows users to define strategies for using multiple tools in sequential or situational modes, making it easier to manage complex tasks like debugging, incident resolution, and code analysis. The server supports YAML configurations for workflows, enabling version control and team collaboration.

This MCP server integrates with Scraper.is, a powerful web scraping tool, allowing AI assistants to extract content from websites in various formats like markdown, HTML, and JSON. It supports features such as real-time progress updates, screenshots, and seamless integration with MCP-compatible AI assistants. Designed for use with tools like Claude Desktop, it facilitates web-based data retrieval for AI workflows.

The AI Meta MCP Server is designed to allow AI models to extend their capabilities by defining and executing custom tools at runtime. It supports multiple runtime environments including JavaScript, Python, and Shell, and ensures security through sandboxed execution and human-in-the-loop approval. The server also features tool persistence, a flexible tool registry, and audit logging for all operations.

This MCP server enables seamless integration with Ableton Live, allowing users to programmatically manage MIDI and audio tracks. It supports features like creating MIDI tracks, adding devices, and composing MIDI notes. The server is built using JavaScript and TypeScript, and it includes tools for debugging and testing. It currently supports Ableton 11 and macOS, with plans to expand to other versions and operating systems.

This project provides a Flutter-based server implementation for the Model Context Protocol (MCP), enabling integration with various platforms and services. It supports multiple platforms including Android, iOS, Linux, macOS, and Windows, making it versatile for cross-platform development. The server is designed to facilitate communication and data handling within applications using the MCP framework.

This repository provides tools for automating Figma design creation and manipulation. It includes a Figma plugin for generating website components, Python scripts for direct file manipulation, and integration with MCP for enhanced functionality. Features include automated navigation bar creation, component templating, and style management.

The Discord MCP Server for AI Assistants is a specialized implementation of the Model Context Protocol (MCP) designed to facilitate interactions between AI assistants and the Discord platform. It provides functionalities such as sending and reading messages, managing channels, handling forum posts, and creating/editing webhooks. This server is particularly useful for integrating AI tools like Claude into Discord, enabling seamless communication and automation within Discord servers.

The Bluesky MCP Server integrates with Bluesky's ATProtocol to enable natural language interactions with Bluesky features. It allows users to fetch posts, analyze feeds, search for content, and even create posts using an LLM-based application. This server can be added to tools like Claude Desktop, turning it into a natural language Bluesky client.

The Flux Schnell Image Generation MCP Server is a TypeScript implementation that leverages Cloudflare's Flux Schnell worker API to generate images from text prompts. It provides a seamless integration with the Flux Schnell API, allowing users to input text descriptions and receive generated image files. The server is configurable via environment variables and supports both project-specific and global configurations in Cursor, making it versatile for various development environments.

This MCP server facilitates seamless integration with AWS CodePipeline, enabling users to manage pipelines efficiently using Windsurf and Cascade. It offers a standardized interface for interacting with AWS CodePipeline services, including listing pipelines, triggering executions, and retrieving metrics. Designed for developers working with AWS, it simplifies pipeline management through natural language requests in Windsurf.