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OpenStack MCP + Agent PoC

@Akrog

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

Akrog

关于 OpenStack MCP + Agent PoC

An OpenStack MCP server PoC

#openstack#mcp#agent#poc

OpenStack MCP + Agent PoC 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-openstack": {
      "command": "uv",
      "args": [
        "run",
        "main.py",
        "--config",
        "../servers.yaml"
      ]
    }
  }
}

OpenStack MCP + Agent PoC 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is OpenStack MCP + Agent PoC?

A proof-of-concept that runs an OpenStack Model Context Protocol (MCP) server alongside a basic agent companion program to test it. It uses the OpenStack code generator to create OpenAPI specs, then serves them with mcp-openapi as one MCP server per OpenStack component. The agent interacts with these servers via an LLM.

How to use OpenStack MCP + Agent PoC?

Clone the repository with submodules, edit servers.yaml to replace placeholder URLs for Nova, Cinder, and Glance, then run the server from the mcp-openapi directory with uv run main.py --config ../servers.yaml. In a second terminal, configure OpenStack client config (e.g., clouds.yaml) and LLM credentials in agent/agent.json, then run the agent with uv run main.py.

Key features of OpenStack MCP + Agent PoC

  • Generates OpenAPI specs from OpenStack using openstack-code-generator.
  • Serves specs as per‑component MCP servers via mcp-openapi.
  • Includes a minimal agent to query OpenStack resources through an LLM.
  • Filters REST API paths to limit tool count and context window.
  • Requires uv package manager, an OpenStack deployment, and an LLM.

Use cases of OpenStack MCP + Agent PoC

  • Quickly test OpenStack API interaction through an LLM.
  • Query OpenStack resources (e.g., flavors, servers) using natural language.
  • Experiment with MCP‑based tool use for OpenStack administration.

常见问题

1

What prerequisites are needed to run it?

You need access to an OpenStack deployment, an LLM endpoint, and the `uv` package manager.

2

How do I configure the OpenStack client?

Place `clouds.yaml` and `secure.yaml` in the `agent/` directory or `~/.config/openstack/` as described in the OpenStack client documentation.

3

Where is the LLM token stored?

Create a file named `llm.token` in the `agent/` directory containing the secret/token for the OpenAI LLM endpoint.

4

Can I use any OpenStack deployment?

Yes, you only need to replace the placeholder URLs in `servers.yaml` with your cluster’s Nova, Cinder, and Glance public endpoints.

5

Does the agent have memory?

No, each prompt is a clean prompt for the LLM—there is no memory between turns.

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