OpenStack MCP Server

Overview

The python‑openstackmcp-server is a Model Context Protocol (MCP) server that bridges AI assistants—such as Claude—to OpenStack clouds. By exposing a standardized MCP interface, the server lets an AI assistant query and manipulate OpenStack resources without needing custom plugins or direct API calls. This solves the common problem of integrating conversational AI with infrastructure management: developers can ask natural‑language questions like “Show me all running instances in the demo project” and receive structured responses that can be used to trigger actions or display dashboards.

At its core, the server implements the MCP specification and translates AI‑generated requests into calls to the OpenStack SDK. It then communicates with the underlying REST APIs of an OpenStack deployment, using credentials supplied via a file. The result is a seamless workflow where the AI assistant can perform compute, image, identity, network, and block‑storage operations in a single conversation. This capability is especially valuable for DevOps teams that want to automate routine cloud tasks, perform rapid troubleshooting, or orchestrate multi‑service deployments through a single AI interface.

Key features of the server include:

• Full MCP support – The server follows the MCP contract, allowing any compliant AI client to discover available tools and invoke them.
• Rich tool set – Separate tool families cover compute (servers, flavors), image management, identity and authentication, networking, and block storage. Each tool exposes CRUD operations, status queries, and resource filtering.
• Credential flexibility – By reading the environment variable, developers can point the server to any OpenStack cloud defined in their , making it easy to switch contexts or use multiple clouds.
• Extensibility – The modular design lets contributors add new OpenStack services or custom actions without altering the MCP contract.

Typical use cases span from on‑the‑fly instance provisioning during a sprint to automated scaling based on AI predictions. In an incident‑response scenario, an operator could ask the assistant to list all instances with high CPU usage and have it automatically reboot or migrate them. For developers, the server enables rapid prototyping of cloud‑aware chatbots that can handle deployment pipelines, perform health checks, or generate cost reports—all through conversational commands.

By integrating directly into AI workflows, the python‑openstackmcp-server removes the friction of manual API interactions. Developers can focus on business logic while the server handles authentication, request translation, and response formatting, resulting in faster delivery of cloud‑native solutions powered by AI assistants.