# Cross - platform

This is a server based on the Model Context Protocol (MCP) for interacting with Audiense Demand accounts, providing tools for creating and analyzing demand reports, tracking entity performance, and obtaining cross - channel and cross - border insights.

A post formatting service from Telegram to X, ensuring that the content meets the X platform's 220 - character limit, retaining key information and enhancing readability.

A simple MCP server for sending desktop notifications, currently supporting the macOS system.

DartMCP is a Model Context Protocol (MCP) implemented in the Dart language, providing server and client components for building interoperable AI tools and services.

A Claude desktop alternative application developed based on Tauri + React + TypeScript, supporting the MCP server protocol

The MCP Easy Installer is a multi - functional tool for searching, installing, configuring, repairing, and uninstalling MCP servers. It supports cross - platform operations and simplifies the development process.

This project is a server based on the MCP protocol that can securely and efficiently execute any terminal commands, support cross - platform operations, and provide system resource information.

A cross - platform tool that supports batch converting PNG/JPG images to WebP format, provides multi - threaded processing and various conversion options, and integrates the MCP protocol for easy use in AI development environments.

The MCP service project for Hugeicons provides icon integration tools and cross - platform documentation support, including functions such as icon search and platform usage guides.

The REAPER MCP Server is a project that exposes the functions of the REAPER Digital Audio Workstation through an API interface. It uses a Lua - Python hybrid architecture, supports file or socket communication methods, and provides 756 tools covering multiple areas of audio processing.

Second Opinion MCP is a multi - AI model consulting platform that allows users to instantly obtain second opinions from different AI models such as OpenAI, Gemini, Grok, Claude, HuggingFace, DeepSeek, and OpenRouter in Claude conversations, supporting functions such as code comparison, creative writing, problem - solving, and cross - model analysis.

Cross - Platform File Search MCP Service

MCP Terminal is a terminal control server designed for AI, providing a standardized interface for AI to execute terminal commands and obtain results.

The Image Gen MCP Server is a general AI image generation service that provides cross - platform and multi - model image generation capabilities for various LLM chatbots through the Model Context Protocol (MCP) standard protocol. It supports multiple image models from OpenAI and Google, enabling seamless conversion from text conversations to visual content.

The Super Shell MCP Server is a cross - platform MCP protocol server for the secure execution and management of multi - platform Shell commands, providing a command whitelist and approval mechanism.

The MCP Manager GUI is a tool that simplifies MCP server management, providing a user - friendly interface, cross - platform support, real - time monitoring, and AI integration features.

CMD MCP Server is a server implementation based on the Model Context Protocol (MCP), which supports executing CMD commands and SSH connections on Windows and Linux systems, allowing the integration of command - line operations with MCP - compatible applications.

Memara MCP integration guide to help users connect AI assistants with Memara to achieve cross - platform memory storage, search, and synchronization functions.

MCP Router is a free cross - platform application that can centrally manage multiple MCP servers, providing secure access control and log statistics functions.

An MCP server that provides persistent Shell command execution capabilities, supporting cross - platform background command execution and session management.

This project provides a server implementation based on the MCP protocol for the Flutter CLI, supporting the creation of Flutter projects for specified platforms or all platforms.

env - mcp is a cross - platform system information acquisition toolkit that supports obtaining detailed hardware, software, and network environment information through the MCP protocol.

A server based on the MCP protocol for sending desktop notifications with sound effects when agent tasks are completed, supporting cross - platform operation.

An MCP service that provides the current time and calculates time differences, supporting Windows and macOS platforms. It returns time data in ISO format through a JSON - RPC interface.

The Local MCP Client is a cross - platform web and API interface tool that interacts with configurable MCP servers through natural language, supports local LLM models and Ollama, and enables structured tool execution and dynamic proxy behavior.

A Python - based MCP server installation tool for cross - platform installation and configuration of various MCP services, supporting multiple clients and local/remote installation methods.

The MCP Log Analysis Server provides cross - platform log reading, filtering, and pagination browsing functions to help debug the interaction issues between Claude and tools.

A professional serial communication MCP server that provides AI assistants with comprehensive serial communication capabilities, supports embedded systems, IoT devices, and hardware debugging, and has cross - platform support and multiple data format processing functions.

The Kiteworks MCP server is a cross - platform service that securely connects LLM applications to the Kiteworks enterprise file storage platform through the model context protocol. It provides file management, folder operation, and user information access functions, supports OAuth 2.0 authentication, and multiple operating systems.

This is an MCP server project that allows users to define AI sub - agents for specific tasks (such as code review, test writing) in Markdown files and execute them in any MCP - compatible tool through the Cursor, Claude Code, or Gemini CLI backends, realizing the reuse of AI sub - agent workflows across IDEs.