"A server for file search, project analysis, and code refactoring on macOS" matching MCP servers:

Provides a Model Context Protocol server for executing AppleScript and JavaScript for Automation scripts on macOS, featuring a knowledge base of pre-defined scripts and supporting automation of macOS applications and system functions.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Control macOS system settings, apps, windows, audio, displays, screenshots, and Focus mode via MCP.

Enables shell command execution on macOS with session management, background processes, caching, and semantic search via Voyage AI.

MCP server that gives Claude Desktop and other desktop MCP clients filesystem powers—read, write, edit, and manage files like AI coding assistants.

A stateful MCP server that remembers what your AI agent has already seen.

Enables comprehensive control over macOS GUI elements including mouse, keyboard, window management, and screen capture. It allows for automated system interactions and script execution while maintaining safety by blocking potentially destructive deletion commands.

A Model Context Protocol server for secure, sandboxed file operations and project scaffolding with built-in search and regex capabilities. It is optimized for Windows environments and features configurable access controls to restrict operations to specific directories.

A FastMCP server that exposes predefined artifacts as Resources and project lifecycle operations as Tools for creating, updating, deploying, debugging, testing, monitoring, and configuring projects with minimal token usage.

Bridges the ChatGPT web UI to a local development environment over MCP, letting the model read and patch files, run real shell commands and terminals, execute tests, and control the desktop (screen, mouse, keyboard, clipboard) within user-approved folders. It adds parallel workers, goal/loop long-task control, plugin tools, and a security-hardening layer with risk-tiered shell policy, audit logging, and credential sanitization.

Connects ChatGPT to local files, terminals, and the desktop through MCP with Chrome integration, letting users read and edit project files, run commands, and follow tool results as they arrive. It also adds durable multi-agent workers, Goal/Loop task tracking, mid-task corrections, and Compact & Resume for carrying session and worker history into a fresh chat.

Enables ChatGPT's web interface to operate a real Mac by executing shell commands and reading or writing files over a reverse SSH tunnel, without installing a client or consuming API quota.

Enables rapid creation of new projects from predefined templates including React, Node.js, Django, Flask, and more. Provides comprehensive project scaffolding with file system operations, template management, and command execution capabilities.

Enables reading and writing macOS system defaults and settings through commands equivalent to the defaults command-line tool. Supports listing domains, searching for settings, and modifying system preferences programmatically.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

Provides a sandboxed Docker environment for executing Python code against API endpoints, exposing tools for discovery, inspection, execution, and reuse.

Enables AI agents to interact programmatically with Claude Code CLI, managing sessions, streaming outputs, and handling permission requests.

Enables AI agents to invoke a local Stata installation, including a Windows Stata build under Wine on Linux and macOS, for regression analysis, econometrics, paper replication, and empirical research with command guards, resource monitoring, and automatic log capture.

Creates short-lived, offline JavaScript sandbox containers for safe code execution via MCP, with no network access and strict resource limits.

Enables building, starting, stopping, restarting, inspecting, and monitoring registered Spring Boot + Maven projects on macOS, including status checks and log tailing through MCP tools.

Enables an AI assistant to operate a Linux machine through tools for files, search, shell commands, long-running processes, git, desktop control, screenshots, and local spreadsheets.

Enables file reading with pagination and regex-based code search across a codebase using grep.

Delegates coding tasks to the local Claude Code CLI via MCP, offering run, review, and status tools for MCP-compatible agents.

Enables MCP clients to browse and edit approved local workspace files, inspect and modify .xlsx spreadsheets, run terminal commands, and use previews and local approvals backed by path guards, backups, and audit logging.

Enables a Claude Desktop or Claude Code session to start, drive, and supervise interactive Claude Code CLI worker runs, including sending prompts, reading terminal output, waiting for completion, stopping runs, and managing settings and permissions.

A local MCP server that lets Hermes supervise Claude Code, delegating focused coding, research, or review tasks to the Claude Code CLI and managing worker sessions, background jobs, cancellations, and read-only reviews.

This server gives AI clients a persistent, isolated Linux container in which the model can run any bash command as root with internet access, install packages and start long-running processes without command filtering. Files produced inside it can be handed back to the user as signed, expiring public download links suitable for PDFs, images, audio and video.

Enables programmatic execution of coding tasks and autonomous file operations using Claude AI. It allows agents to search codebases, run shell commands, and track file changes through the Model Context Protocol.

GDB Enhanced Features MCP server for remote debugging and CTF exploitation with 41 specialized tools including ROP search, format-string detection, and memory patching.

Enables orchestrating models to delegate reading and answering questions about local files to a local LLM, avoiding sending file contents through the orchestrator's context.