"Elastic Cloud" matching MCP servers:

Enables AI assistants and other MCP clients to launch and steer Cursor Cloud Agents on repositories or as repo-less research tasks, including model selection, mid-run follow-ups, cancellation, result retrieval, and run listing. Also exposes authentication checks and per-agent or per-run token usage and cost reporting.

Enables AI assistants and MCP clients to launch and manage Cursor Cloud Agents on repositories or without one, select models, follow up mid-run, retrieve or cancel results, and check usage and cost.

Enables AI agents to control NetEase Cloud Music playback, login, search, and get personalized recommendations through official APIs.

A Model Context Protocol server that enables agent applications like Cursor and Cline to integrate with Alibaba Cloud Function Compute, allowing them to deploy and manage serverless functions through natural language interactions.

Enables AI assistants to interact with PingOne Advanced Identity Cloud environments through natural language, supporting user management, authentication theme customization, log analysis, and identity data queries with secure OAuth 2.0 authentication.

Enables AI assistants to create and manage Cursor Cloud Agents that autonomously work on GitHub repositories, including creating tasks, monitoring progress, and automatically generating pull requests.

Enables AI to execute RPA applications and workflows through the Model Context Protocol, supporting both local and cloud modes.

Unifies 7,283+ detection rules from Sigma, Splunk ESCU, Elastic, and KQL into a single queryable interface via MCP, with a web dashboard and autonomous agent pipeline for detection engineering.

Paid remote MCP for browser session management, enabling AI agents to open sessions, run stateful snippets, read page state, and export session logs.

Enables autonomous agents to detect and clean up idle or abandoned cloud GPU/disk resources for FinOps cost optimization.

Creates and manages Claude Code cloud environments, agents, and sessions programmatically via the Anthropic Managed Agents API.

Give your AI agent its own cloud computer: create and drive hosted plori agents over MCP (invoke agents, read replies, human-in-the-loop, scheduled runs). This repo is the integration front door and ships the npx plori-mcp stdio bridge to the hosted endpoint.

Engram is a local-first memory server that lets AI agents remember things across sessions with zero cloud dependencies.

Procedural memory for coding agents: not facts about the user, but how a task was solved — trigger, steps, outcome, lessons. Hooks recall it automatically; skills strengthen only on outside evidence and unused ones decay. Each memory carries its provenance and only the owner approves one as a rule. Local SQLite + FTS5, no API key, no cloud. One database for Claude Code, Codex and Cursor.

Enables AI agents to capture and analyze screenshots of macOS applications, windows, or the entire screen using local (Ollama) or cloud-based AI vision models, with non-intrusive, fast screen capture via Apple's ScreenCaptureKit.

Provides a sequentialthinking tool for dynamic, reflective problem-solving via chain-of-thought reasoning. Supports local Stdio and remote SSE deployment on Google Cloud Run.

Stdio MCP Server wrapping custom Python runtime (LocalPythonExecutor) from Hugging Faces' smolagents framework. The runtime combines the ease of setup (compared to docker, VM, cloud runtimes) while providing safeguards and limiting operations/imports that are allowed inside the runtime.

MCP server that enables running and managing file-based AI Skills, Agents, and Flow workflows as DAGs, with tools for listing, executing, and resuming tasks via stdio JSON-RPC.

engRAM provides encrypted, offline, RAM-resident memory for AI agents via MCP, enabling fast hybrid search, deterministic importance-based recall, and secure vault management with tools like memory_search, memory_store, and memory_forget.

An MCP server that enables users to query, compare, and synthesize responses from multiple local and cloud LLMs simultaneously using existing subscriptions. It provides tools for parallel model evaluation, consensus polling with an LLM-as-judge, and response synthesis across different model providers.