"Understanding the word 'process' or its usage" matching MCP servers:

Enables AI agents to directly manipulate Jupyter Notebook cells and structure through a secure, well-defined API, overcoming Cursor's AI agent mode limitations.

A Model Context Protocol (MCP) server that allows Claude AI to interact with custom tools, enabling extension of Claude's capabilities through the MCP framework.

Enables asynchronous and parallel execution of Claude Code tasks across multiple sessions, allowing users to start background tasks and continue working immediately without blocking.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

Enables strict resource governance for AI agent processes using Linux cgroup v2, enforcing CPU bandwidth, memory limits, and process limits to prevent resource overconsumption, runaway loops, and fork bombs.

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution

Resource governor configuring Linux cgroup v2 controllers and arbitrating resource overconsumption, runaway loops, and fork bombs.

Enables a bot to inspect, query, create, edit, and script Roblox Studio instances in token-cheap batches, plus run Luau, playtests, screenshots, and inputs through Roblox's official Studio MCP connection. It runs locally over stdio or an authenticated local HTTP endpoint, returning small structured answers instead of dumping whole games into context.

An MCP server that lets AI models run code in 31 languages, evaluate symbolic math and logic problems, and measure complexity—exposed as 48 tools for execution, session management, translation, optimization, and more.

Delegates coding tasks to the locally installed Kimi Code CLI, reusing its auth, models, and configuration while adding tools for sessions, replies, model discovery, live turn steering, and history access.

A secure mathematical computation sandbox that enables LLMs to perform symbolic math operations like algebra, calculus, and equation solving via SymPy. It features low-latency execution through pre-warmed process pools and provides standardized JSON outputs for reliable agent integration.

Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.

Enables AI coding agents to safely execute commands, run tests, and modify project files inside disposable, policy-enforced Docker sandboxes that are isolated from the host machine and its credentials.

An MCP server that enables coding agents to execute and manage long-running shell commands asynchronously with capabilities for process monitoring, interaction, and lifecycle management.

Enable Claude Code, Cursor, or your favorite LLM to interact with Xcode, building your projects the same way you do, and seeing the same errors. Greatly increases productivity when working on iOS, iPadOS, macOS, visionOS, tvOS projects & Swift packages - or any time you might use Xcode.

Enables an AI agent to make music with SuperCollider by rendering code to WAV files offline with peak and RMS level stats, plus realtime playback and stopping. Runs on macOS and Linux via a subprocess, with per-call process isolation so crashes or hangs can't take the server down.

Enables execution of TypeScript code to call MCP tools instead of direct tool calls, reducing token usage by up to 98% while orchestrating complex multi-tool workflows through secure sandboxed code execution.

A proxy MCP server that wraps MCP-for-Unity, correcting transport inaccuracies and restricting tool usage for reliable Unity integration.

Orchestrates multiple MCP servers through a Deno sandbox, reducing token usage by 99.7% by processing tool calls and returning only compact results.

Enables AI assistants to see, control, and debug PySide6 desktop applications without modifying their source code, by capturing screenshots, inspecting widget trees, performing clicks and typing, reading logs, and running Python inside the app process.