Overview of the Gcore MCP Server

The Gcore MCP Server bridges AI assistants with the Gcore Cloud API, enabling developers to perform cloud operations directly from natural‑language prompts. By exposing a curated set of tools—ranging from virtual machine management to GPU cluster provisioning—the server lets large language models act as a first‑class interface for cloud infrastructure, eliminating the need to write raw API calls or scripts.

This MCP server solves a common pain point: the complexity of cloud provider APIs. Developers often struggle to remember endpoint names, authentication headers, or required payload structures. The server abstracts these details into high‑level tool calls that the model can invoke automatically. It also mitigates confusion for the assistant by allowing selective tool exposure; developers can configure which capabilities are available, ensuring that the model only sees relevant commands for a given task.

Key features include:

• Unified configuration via the environment variable, supporting predefined toolsets (e.g., , ) and custom wildcard patterns (, ).
• Multiple transport modes (HTTP and stdio) with sensible defaults that adapt to the chosen communication channel.
• Priority‑based tool selection where explicit toolsets override pattern matches, preventing accidental exposure of unintended methods.
• Extensive tool coverage: from core account management to AI/ML inference services, allowing a single assistant to orchestrate end‑to‑end workflows.

Real‑world use cases span automated deployment pipelines, cost monitoring dashboards, and on‑demand GPU cluster provisioning for machine‑learning experiments. A data scientist could ask the assistant to “spin up a GPU cluster with 8 GPUs in region 76” and receive an instant, authenticated API call without touching the console. Similarly, a DevOps engineer could request “list all active instances and their billing details,” receiving a consolidated report that the assistant can format or visualize.

Integrating with existing AI workflows is straightforward. Developers add the server to their Cursor IDE configuration or launch it via a simple command, then pass and optional project/region identifiers. Once running, the assistant can invoke tools through the MCP action, and the server translates these into authenticated Gcore SDK calls. This tight coupling preserves security—API keys remain on the server side—and keeps model interactions stateless and reproducible.

In summary, the Gcore MCP Server transforms raw cloud APIs into conversational commands, offering a flexible, secure, and developer‑friendly gateway for AI assistants to manage infrastructure, automate tasks, and streamline cloud operations.