"Using Cursor to Find Suitable Repositories on GitHub" matching MCP servers:

Enables MCP clients to run Cursor CLI agent prompts with any model the installed CLI offers, continue existing sessions, and list available models and sessions. Adds guarded execution with concurrency limits, timeouts, cancellation, and operator-gated auto-approval so multiple models can be run in parallel.

An MCP server that enables searching for specific files by name within the current directory and its subdirectories. It uses Server-Sent Events (SSE) to provide a find_file tool for locating local files and libraries.

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 natural language management of MikroTik routers through Cursor IDE by translating requests into RouterOS commands via API or SSH.

Enables Grok Bot to securely communicate with a local Cursor Agent CLI through an OAuth-authenticated MCP gateway, exposing exactly two tools for asking questions and checking status.

An MCP server wrapping the Cursor CLI agent, enabling Claude Code and other MCP clients to delegate tasks to Cursor's AI agent for file writing, bash commands, and codebase queries.

Connects Windows-based Codex to the Cursor Agent CLI in WSL, providing a run_cursor tool to execute tasks in specified working directories and resume sessions per directory.

Converts any Python Click or Typer CLI into an MCP server automatically — zero-code transformation that introspects CLI commands and exposes them as MCP tools.

Provides 29 Git operations and 11 workflow combinations for AI assistants and developers, enabling comprehensive Git repository management through a standardized interface.

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.

Wraps GitHub Copilot CLI as an MCP server, enabling MCP clients to run Copilot conversations for code tasks with support for session resumption and permission modes.

MCP server that routes Claude Desktop chat into Claude Code, enabling headless execution and terminal handoff with session continuity.

Enables AI agents to securely connect to and manage remote servers via SSH, with tools for running commands, file operations, log inspection, service management, and system monitoring under configurable access controls and audit logging.

Enables AI assistants to diagnose, troubleshoot, and manage PC settings on Windows, macOS, and Linux through natural language commands.

Gives ChatGPT hands-on control of a local machine: reading, patching and executing code in approved project folders, running real interactive processes, managing plans and worker sub-chats, and driving the browser or desktop through user-granted permissions. Every capability is a switch inside an approved-folder sandbox, so nothing runs that the user did not explicitly enable.

An MCP server that lets Claude query Google's Gemini CLI as a sub-agent. Get second opinions, verify information, and cross-reference answers between AI models.

Wraps the GitHub Copilot CLI to provide natural language command suggestions, explanations, and shell configuration for shell, git, and GitHub CLI commands through the Model Context Protocol.

Enables arithmetic operations (add, subtract, multiply, divide) through a custom Model Context Protocol implementation, containerized with Docker for easy deployment.

Enables cloud agents to securely operate local machine resources (files, commands, screenshots) via standard MCP protocol.

Enables ChatGPT to work on a local project by reading and editing files, running shell commands, keeping terminals and desktop sessions open, and splitting work across durable workers whose context persists between tasks. Long-running work can be steered mid-flight, tracked with Goal, and carried into fresh chats via Compact & Resume.