Redfish MCP Server

Redfish MCP Server

Overview

The Redfish MCP Server is a natural language interface designed for agentic applications to efficiently manage infrastructure that exposes Redfish API for this purpose. It integrates seamlessly with MCP (Model Content Protocol) clients, enabling AI-driven workflows to interact with structured and unstructured data of the infrastructure. Using this MCP Server, you can ask questions like:

• "List the available infrastructure components"
• "Get the data of ethernet interfaces of the infrastructure component X"

Features

• Natural Language Queries: Enables AI agents to query the data of infrastrcuture components using natural language.
• Seamless MCP Integration: Works with any MCP client for smooth communication.
• Full Redfish Support: It wraps the Python Redfish library

Tools

This MCP Server provides tools to manage the data of infrastructure via the Redfish API.

• list_endpoints to query the Redfish API endpoints that are configured for the MCP Server.
• get_resource_data to read the data of a specific resource (e.g. System, EthernetInterface, etc.)

Installation

Follow these instructions to install the server.

# Clone the repository
git clone 
cd mcp-redfish

# Install dependencies using uv
uv venv
source .venv/bin/activate
uv sync

Configuration

To configure this Redfish MCP Server, consider the following environment variables:

There are several ways to set environment variables:

1. Using a .env File: Place a .env file in your project directory with key-value pairs for each environment variable. Tools like python-dotenv, pipenv, and uv can automatically load these variables when running your application. This is a convenient and secure way to manage configuration, as it keeps sensitive data out of your shell history and version control (if .env is in .gitignore).

For example, create a .env file with the following content from the .env.example file provided in the repository:

cp .env.example .env

Then edit the .env file to set your Redfish configuration

OR,

2. Setting Variables in the Shell: You can export environment variables directly in your shell before running your application. For example:This method is useful for temporary overrides or quick testing.

Transports

This MCP server can be configured to handle requests locally, running as a process and communicating with the MCP client via stdin and stdout.This is the default configuration. The sse and streamable-http transport is also configurable so the server is available over the network.Configure the MCP_TRANSPORT variable accordingly.

export MCP_TRANSPORT="sse"

Then start the server.

uv run src/main.py

Test the server:

curl -i http://127.0.0.1:8000/sse
HTTP/1.1 200 OK

Integrate with your favorite tool or client. The VS Code configuration for GitHub Copilot is:

"mcp": {
    "servers": {
        "redfish-mcp": {
            "type": "sse",
            "url": "http://127.0.0.1:8000/sse"
        },
    }
},

Integration with Claude Desktop

Manual configuration

You can configure Claude Desktop to use this MCP Server.

1. Retrieve your uv command full path (e.g. which uv)
2. Edit the claude_desktop_config.json configuration file
   • on a MacOS, at ~/Library/Application\ Support/Claude/

{
    "mcpServers": {
        "redfish": {
            "command": "<full_path_uv_command>",
            "args": [
                "--directory",
                "<your_mcp_server_directory>",
                "run",
                "src/main.py"
            ],
            "env": {
                "REDFISH_HOSTS": "<the_list_of_your_redfish_endpoints>",
                "REDFISH_PORT": "<your_redfish_port>",
                "REDFISH_PASSWORD": "<your_redfish_password>",
                "REDFISH_SERVER_CA_CERT": "<your_redfish_server_ca_path>",
            }
        }
    }
}

Troubleshooting

You can troubleshoot problems by tailing the log file.

tail -f ~/Library/Logs/Claude/mcp-server-redfish.log

Integration with VS Code

To use the Redfish MCP Server with VS Code, you need:

1. Enable the agent mode tools. Add the following to your settings.json:

{
  "chat.agent.enabled": true
}

2. Add the Redfish MCP Server configuration to your mcp.json or settings.json:

// Example .vscode/mcp.json
{
  "servers": {
    "redfish": {
      "type": "stdio",
      "command": "<full_path_uv_command>",
      "args": [
        "--directory",
        "<your_mcp_server_directory>",
        "run",
        "src/main.py"
      ],
      "env": {
        "REDFISH_HOSTS": "<the_list_of_your_redfish_endpoints>",
        "REDFISH_PORT": "<your_redfish_port>",
        "REDFISH_PASSWORD": "<your_redfish_password>",
      }
    }
  }
}

// Example settings.json
{
  "mcp": {
    "servers": {
      "redfish": {
        "type": "stdio",
        "command": "<full_path_uv_command>",
        "args": [
          "--directory",
          "<your_mcp_server_directory>",
          "run",
          "src/main.py"
        ],
        "env": {
          "REDFISH_HOSTS": "<the_list_of_your_redfish_endpoints>",
          "REDFISH_PORT": "<your_redfish_port>",
          "REDFISH_PASSWORD": "<your_redfish_password>",
        }
      }
    }
  }
}

For more information, see the VS Code documentation.

Testing

You can use the MCP Inspector for visual debugging of this MCP Server.

npx @modelcontextprotocol/inspector uv run src/main.py

Example Use Cases

• AI Assistants: Enable LLMs to fetch infrastructure data via Redfish API .
• Chatbots & Virtual Agents: Retrieve data, and personalize responses.