"A managed cloud provider that deploys my application" matching MCP servers:

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

A Model Context Protocol server built with FastMCP that enables dynamic tool loading and configuration from individual Python files. It provides a flexible framework for automatically discovering, testing, and running tools via Stdio or HTTP transport modes.

An MCP server framework featuring dynamic tool loading and a modular one-tool-per-file architecture for rapid development. It supports both Stdio and HTTP transport modes, offering automated test generation and centralized configuration management.

A minimal MCP server template for learning NitroStack, featuring a calculator module with tools, resources, and prompts for arithmetic operations.

A Model Context Protocol (MCP) server that connects AI agents to Google Workspace (Gmail, Calendar, Drive, Docs, Sheets, and Slides).

A personal memory engine and MCP server that stores durable facts in markdown files managed via git, enabling hybrid search (lexical + semantic) through an MCP interface for persistent context across LLM sessions.

MCP server that enables cloud models (like Gemini/Claude) to delegate coding tasks to a local llama.cpp server, preserving cloud usage limits through an AI-powered code review loop.

MCP server that establishes feedback-oriented development workflows with dual Web UI and desktop application interfaces, enabling AI to confirm with users via prompts and real-time feedback to reduce speculative operations and improve efficiency.

Multi-provider image generation MCP server that enables image generation from Claude Desktop, Claude Code, or any MCP client using OpenAI, Google Gemini, Stable Diffusion, or a placeholder provider.

MCP server that enables accessing Jira and Confluence using browser cookies captured via a Chrome extension, avoiding the need for API tokens or browser automation. It wraps the mcp-atlassian toolset and works with Atlassian Cloud and Server/Data Center behind SSO.

An MCP server that enables LLMs to execute Python code on GPU-accelerated compute nodes within SLURM-managed HPC environments. It bridges local clients to remote clusters by launching JupyterLab sessions via SLURM jobs to facilitate high-performance notebook-based computation.

A FastMCP-based server providing a comprehensive toolset for the RunningHub AI application platform. It enables users to configure nodes, upload media, submit AI tasks, and manage task execution and results through persistent storage.

MCP server for Coalesce that manages nodes, pipelines, environments, jobs, and runs, and enables project validation, DDL/DML preview, deployment planning, and cloud environment application.

An MCP server that integrates fal.ai's image generation and editing capabilities into MCP-compatible clients. It enables text-to-image generation, style application via LoRAs, and image editing using natural language instructions.

Enables coding agents to consult, design, implement, and review with an independent AI peer via Oh My Pi, using persistent project-isolated conversations and structured MCP tools for thinking, analysis, and authorized work.

MCP server for Preset (managed Apache Superset). Manage dashboards, charts, and datasets from Claude Code and other LLM agents.

Enables financial data retrieval, company context, market information, financial modelling, and valuation through MCP tools, with a deterministic sample provider for testing before connecting live data sources.

A comprehensive MCP server for Jira, Confluence, and Bitbucket that supports both Cloud and Data Center deployments. It provides 136 tools for managing issues, searching documentation, and handling Git workflows like pull requests and pipelines.

MCP server that parses web pages into clean markdown via Crawl4AI's HTTP API, exposing parse_page and health-check tools with configurable proxy and timeout.

Spawn parallel autonomous AI agent sessions from a single MCP client. Each agent gets its own workspace, tools, and execution context.