Azure HPC/AI MCP Server

A minimal Model Context Protocol (MCP) server tailored for Azure HPC/AI clusters. It provides tools that query Kubernetes for GPU node information using kubectl. The server is implemented with fastmcp and uses synchronous subprocess calls (no asyncio).

Tools

• list_nodes: Lists nodes in the GPU pool with name, labels, and Ready/NotReady status.

• get_node_topologies: Returns InfiniBand-related topology labels per node: agentpool, pkey, torset.

Both tools shell out to kubectl and return JSON-serializable Python structures (lists of dicts).

Related MCP server: Azure Resource MCP Server

Run the server

Prerequisites:

• Python 3.10+

• kubectl configured to access your cluster

Installation

It’s recommended to use a virtual environment.

Create and activate a venv (Linux/macOS):

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -U pip

Install dependencies with pip:

pip install -r requirements.txt

Notes:

• fastmcp is required to run the server and is installed via requirements.txt. Tests don’t need it (they stub it).

• If fastmcp isn’t on PyPI for your environment, install it from its source per its documentation.

Run:

python server.py

The server runs over stdio for MCP hosts. You can connect to it with an MCP-compatible client or call the tools locally with the helper script below.

invoke_local helper

The invoke_local.py script lets you execute server tools in-process without an MCP host. It discovers exported tools from server.py, calls them synchronously, and prints pretty JSON.

Examples:

# List nodes
python invoke_local.py list_nodes

# Get IB topology labels
python invoke_local.py get_node_topologies

# Passing parameters (if a tool defines any):
python invoke_local.py some_tool --params '{"key":"value"}'

Implementation notes:

• No asyncio is used; tool functions call subprocess.run directly and return plain Python data.

• The script unwraps simple function tools or FastMCP FunctionTool-like wrappers and invokes them with kwargs from --params when provided.

Tests

The tests are written with pytest and exercise success and error paths without requiring a cluster.

Key points:

• subprocess-based: Tests monkeypatch subprocess.run to simulate kubectl output and errors. There is no usage of asyncio in code or tests.

• fastmcp-free: Tests inject a lightweight dummy FastMCP module so importing server.py does not require the real dependency.

• Coverage: Both tools are validated for JSON parsing, Ready condition handling, missing labels, and kubectl failures.

Run tests:

python -m pip install -U pytest
pytest -q

Troubleshooting

• kubectl not found: Ensure kubectl is installed and on PATH for real runs. Tests do not require it.

• No nodes returned: Confirm your label selectors match your cluster (tools currently expect GPU/IB labels used in Azure HPC/AI pools).

• fastmcp import error: Install fastmcp for runtime; tests provide a dummy stub so you can run pytest without it.