"A server for local file operations on Windows that integrates with Ollama" matching MCP servers:

Enables Windows users to connect Antigravity CLI as an MCP subagent for coding tasks such as file reading, code execution, edits, and terminal commands, with model selection, conversation continuity, and streaming progress.

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.

A lightweight server that enables AI agents to interact natively with the Windows operating system for tasks like UI automation and application control. It allows LLMs to perform file navigation, simulate user input, and manage windows without requiring specialized computer vision models.

A local MCP server that gives Ollama language models sandboxed file management tools—read, write, list, and recoverable delete—within configurable workspace directories, keeping everything local and secure.

Enables launching and controlling real, visible Windows Telnet windows by reading their screen buffers and sending keyboard events, with session management and text-waiting capabilities.

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

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.

Provides comprehensive Windows system control including file operations, process management, window control, screenshots, clipboard access, PowerShell/CMD execution, and optional mouse/keyboard automation and browser control capabilities.

A lightweight Windows automation MCP server that scopes UI operations to a single target window, offering tools like ReadWindow, PeekWindow, Click, Type, Scroll, Screenshot, and App management with reduced token usage compared to full-desktop alternatives.

Enables ChatGPT to safely interact with Windows workspaces through a loopback-only MCP server, with OAuth, bounded PowerShell tools, persistent jobs, and optional Cloudflare Tunnel exposure.

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

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.

Enables supervised PowerShell execution on Windows, allowing the DeepSeek agent to run local commands and manage files safely through a single tool.

Enables AI assistants to find and launch Windows applications by name using multi-strategy discovery via registry, Start Menu, PATH, and common directories.

Enables Claude to fully control a Windows desktop with native screen capture, low-level input injection, game-grade input, and play-testing capabilities.

Enables batched filesystem operations on allowed directories and standalone Windows shell command execution, including background jobs and interactive console windows, through MCP tools.

Enables secure remote development on Windows by allowing LLM clients to manage files, run commands, and automate browsers within sandboxed workspaces, with multi-project switching and OAuth authentication.

Turns your local machine into an MCP server with a GUI, allowing agents to execute file operations, run commands (including admin), manage background processes, and query databases, with optional ngrok exposure.

A local Python MCP server that provides basic tools like greet, add_numbers, and system_info, designed for process-based connections via stdin/stdout.

Enables cloud-based MCP clients to delegate privacy-sensitive or large-output tasks to a local LLM, returning sanitized placeholders or digested summaries so private data never leaves the user's network.