Everpure now offers a Pure1® Manage MCP Server so you can query information about your fleet using natural language questions.
In this post, I’ll explain how the Pure1 Manage MCP Server works. The first section will explain MCP in general, and the second section will explain how to use our specific server. Feel free to skip to the Quick Start section if you’re already familiar with MCP and just need the parameters to plug into your host.
What is MCP?
MCP stands for "Model Context Protocol," and it's a way for users to connect their AI applications to external systems using tool calls.
MCP tools are fundamentally rooted in application programming interfaces (APIs). An API is a set of rules and protocols that allows different software applications to communicate with each other. It acts as an intermediary, enabling one piece of software (the client) to request information or functionality from another piece of software (the server) without needing to know the server's internal workings. For instance, when you check the weather on your phone, the weather app uses an API to send a request to a weather service, which then returns the current weather data.
AI applications have trouble making API calls directly because APIs are designed for completeness and correctness, not for an LLM to use easily. When an AI application wants to use an external system to handle a user’s request, it uses the MCP protocol to make a tool call. The AI (client) requests a function (the tool) from an external system (the server), and the system executes the function and returns a result. This makes MCP a system that standardizes and mediates API-like interactions, allowing AI models to leverage external, real-world capabilities.
For more information, see this article on the MCP website: “What is the Model Context Protocol (MCP)?”
How can customers benefit from the Pure1 Manage MCP Server?
The Pure1 Manage MCP Server enables customers to securely integrate AI assistants, copilots, and agentic systems with live Pure1 telemetry and operational data—without building custom API integrations. It transforms Pure1 from a dashboard-centric experience into an AI-accessible platform, enabling natural language interaction, contextual automation, and real-time operational intelligence. Customers benefit from faster AI integration, reduced engineering effort, preserved security controls, and improved decision velocity across hybrid environments.
What types of customer workflows are best suited for MCP?
The Pure1 Manage MCP Server is particularly well-suited for agentic and AI-driven workflows, including:
Fleet telemetry integration with customer copilots
Expose Pure1 telemetry—arrays, volumes, workloads, metrics, and alerts—into internal copilots, chatbots, or AI platforms via MCP endpoints.
Value: Unified operational visibility across hybrid and multi-platform environments
Automation with context awareness
Use MCP to validate storage state, health, performance, or capacity before executing provisioning, backup, or disaster recovery workflows.
Value: Safer automation with contextual validation, reduced execution errors, and greater rollback confidence
Hybrid cloud observability
Correlate Everpure array performance and capacity metrics with application, VM, container, or cloud telemetry across environments.
Value: Faster troubleshooting and improved end-to-end performance insights
Conversational operational analytics
Enable operators to ask real-time natural language questions, such as:
- “Which arrays are nearing capacity risk?”
- “Show me the top latency spikes in the last 24 hours.”
- “Summarize all critical alerts across regions.”
Value: Rapid insight without navigating dashboards or exporting reports
What the Pure1 Manage MCP Server can do
The Pure1 Manage MCP Server exposes the Pure1 Manage REST APIs to AI applications. This means you can ask ad hoc questions about your fleet using natural language, and your AI application will use the server’s tools to query the answer. No programming required.
Example conversation
Here’s an example conversation demonstrating the use of the Pure1 Manage MCP Server:
User: "What is the name of my oldest FlashArray and what is its serial number?"
Chatbot:Processing Request: The AI recognizes this as a request for fleet information requiring a tool call.
Tool Invocation: Get_hardware
Response: The oldest FlashArray in your fleet is named FA-Prod-01 with serial number FA10123456.
User: "Can you show me the average I/O latency for that array over the last 7 days?"
Chatbot: Let me first check the available latency metrics, then query the historical data:
Tool Invocation: Get_metrics
Tool Invocation: Get_metrics_history
Response: The average I/O latency for FA-Prod-01 (serial FA10123456) over the last 7 days was 0.385 milliseconds.
User: "How many arrays do I have in total that are still under warranty?"
Chatbot: Processing Request: The AI recognizes this as a request for a count of arrays filtered by warranty status.
Tool Invocation: Get_array_support_contracts
Response: You currently have 12 arrays in your fleet that are still under active warranty.
Quick start
Step 1: Register an API key in Pure1 Manage
The Pure1 Manage MCP Server leverages the Pure1 Manage REST APIs. In order to access those APIs, you need to register an API key in Pure1 Manage. To do that, follow the directions in the The Pure1® REST API introductory blog post.
After going through the instructions, you will have an application id and a private key file, which will be used to generate an access token to access the MCP server in step 2.
Step 2: Set up the pure1_token_factory.py script
Prerequisites: you need Python 3.12 or greater to run the script.
- Download pure1_token_factory.zip.
- Unzip the archive.
- Go to the unzipped folder in your command-line terminal.
- Optional but recommended: create and activate a Python virtual environment:
-
python3 -m venv .venv
-
source .venv/bin/activate
- Install the requirements:
-
pip3 install -r requirements.txt.
-
Run python3 pure1_token_factory.py <application_id> <private_key_file>
- Copy the generated access token from the script output for the next step.
Step 3: Add remote MCP server to your AI application
Follow the directions for your AI application to add a remote MCP server (see the Pure1 Manage MCP Server User Guide for instructions for specific chatbots). In general, they need the following information:
- Remote MCP Server address: https://api.pure1.purestorage.com/mcp
- Authorization type: header
- Header name: Authorization
- Header value: Bearer <access-token>
- Important: <access-token> is just a placeholder for the access token you generated in step 2.
- The actual header value should look something like “Bearer eyJ0eXAiO…”
Important: you need to generate a new access token every 10 hours and copy it into your AI application
You’ll need to run pure1_token_factory.py to generate a new access token every 10 hours, and manually copy the access token into your AI application’s config.
Claude Desktop instructions
Claude Desktop is a special case because it doesn’t let you set the Authorization header directly. You have to run the mcp-remote local MCP server and configure that to use the Pure1 Manage remote MCP server.
Prerequisites
You need to have Node.js version 18 or newer installed on your system.
Configuration
In Claude Desktop, go to Settings > Developer, and click Edit Config. Open the claude_desktop_config.json file in a plain-text editor like VS Code.
Configure the mcp-remote server, which is necessary to pass the Authorization header to the Pure1 Manage MCP Server.
Paste the token into the configuration file, then restart Claude Desktop.
{
"mcpServers": {
"Pure1 API": {
"command": "npx",
"args": [
"-y",
"mcp-remote",
"https://api.pure1.purestorage.com/mcp",
"--header",
"Authorization:${AUTHORIZATION_HEADER}"
],
"env": {
"AUTHORIZATION_HEADER": " Bearer <paste access token here>"
}
}
}
Note: there might be other configuration options in this file. Be sure to leave them unchanged, and only insert the Pure1 API config in the mcpServers section.
The space in the AUTHORIZATION_HEADER environment variable is important. It's there to work around a bug in Windows argument parsing.
Please note that:
- The first time it uses a tool, it will ask you for permission.
- You can grant permission to all tools at once by going to Customize > Connectors > Pure1 API, and selecting Always Allow under Other tools.
For more detailed instructions from Anthropic, please refer to: Connect to local MCP servers - Model Context Protocol.
