A macOS-native bridge server that enables communication between different AI clients like Claude and Cline, allowing them to interact with each other through the Model Context Protocol.
Enables Codex to delegate tasks to interactive Claude Code sessions as role-controlled subagents for exploration, code review, and implementation, using the existing Claude Code installation on macOS.
Provides an MCP server for macOS that allows agents to control a managed browser with accessibility-first actions, visible cursor, and screenshot return.
Bridges Model Context Protocol clients to your actual Chromium browser, enabling control of tabs, reading authenticated pages, cookies, and storage, and running JavaScript on approved sites. Emphasizes security with per-site approval, high-risk action confirmations (including Touch ID on macOS), and an attested native-messaging bridge.
Enables autonomous desktop automation by delegating tasks to vision-based agents operating within cloud-based virtual machine sandboxes. It allows users to manage VMs, execute complex computer tasks, and receive text-based screen summaries across Linux, Windows, and macOS environments.
MCP server that lets AI coding agents report task progress and milestones, providing a real-time web dashboard and macOS notifications for monitoring multiple AI workbenches.
Bridges Claude with Perplexity's Comet browser for autonomous web browsing, research, and multi-tab workflow management. Supports dynamic content interaction, login wall handling, file uploads, and intelligent completion detection across Windows, macOS, and WSL platforms.
Provides AI-aware failover for Mihomo proxy on macOS by validating real OpenAI paths and switching proxy groups after verified hard failures. It offers a local stdio MCP server for diagnostics, status, and safe management of the AI proxy group.
Hybrid computer-use MCP server that automates browsers and native macOS/Linux apps by fusing accessibility trees, Chrome DevTools Protocol, and vision into one structured context. Exposes four composable tools — cel_see, cel_act, cel_think, cel_perceive — that work with any MCP client and run fully offline via Ollama.
Exposes vLLM capabilities to AI assistants, enabling chat completions, model management, and platform-aware container control with automatic detection of Docker/Podman and GPU availability across Linux, macOS, and Windows.