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Greetings, I am just starting my Pure1 Workflows journey, and wanted to know some "Recipe ideas" others would recommend for beginners in order to get the hang of how to create them before progressing onto more complex ones? Example, setting up monitoring, provisioning volumes, etc...First Preset and Counting
A bit routine, but this preset creates a volume to be used as a VMware datastore with a set regular size of 16GB. It also ensures "VMware Datastore" is in the volume name to quickly ascertain what the volume's use case is. Lastly, snapshots are taken every 6 hours and retained for 5-days. Create Regular Size 16GB Volume for a VMware Datastore w/6h5d SnapshotsUsing AI to Help Manage your Data Infrastructure (Ask Us Everything Recap)
AI is showing up everywhere in IT. But for storage admins, the real question is whether it can actually help save time and make managing infrastructure easier. That was the focus of our latest Ask Us Everything, where Everpure experts Anand Madhavan (Director of Product Management), Ameerul Shah (Product Manager) and Russell Cagle (Tech Lead), fielded questions from the Everpure Community about putting AI to work in real-world storage environments. What can Pure1 AI Copilot actually do? The discussion started with the basics: How was this tool designed to help IT professionals? Anand described it as the next step in Everpure’s long-running focus on simplicity. Instead of searching documentation, digging through multiple screens, or remembering commands, IT folks can ask questions in natural language. Pure1 AI Copilot uses information from Everpure knowledge sources along with data about your own fleet to help find the answers you need. That means you can ask important things like: What’s the latency on this array? Which arrays need an upgrade and why? ? What alerts should I be concerned about? No deep knowledge is needed to get actionable answers. Importantly, Pure1 AI Copilot is currently focused on finding answers, and giving you options. It can reason out a problem and recommend what to do next, but it doesn’t simply start executing commands. And this is by design: Everpure’s approach separates AI reasoning from deterministic execution, so there aren’t any surprises. Pure1 AI Copilot works with capabilities such as Workflow Orchestration and Everpure FusionTM Presets to automate tasks, so you stay in control. Can AI really make troubleshooting faster? Consider that familiar question: “My array was slow last night. What happened?” Traditionally, answering it might require piecing together telemetry, documentation, CLI information, alerts, and possibly opening a support case. Triage Assistant is designed to work through that investigation process for you—checking alerts and upgrades, looking for latency spikes, identifying busy volumes, and then providing evidence and recommended next steps. Ameerul shared that an investigation that might otherwise stretch across a day or two could potentially produce useful answers through Copilot in a matter of minutes. That’s the kind of AI benefit storage teams can appreciate: less hunting for information and more time spent on solving problems. What if I want to use my own AI assistant? This is where the conversation turned to MCP servers. Russell explained that the Pure1 Manage MCP server gives compatible AI assistants (like Claude, Cursor, etc.) access to Pure1 Manage data. That opens some intriguing possibilities because storage rarely operates in isolation. When asked whether Pure1 AI Copilot could understand environments beyond Everpure, such as VMware and Azure, the answer highlighted the bigger opportunity behind MCP. That is, connecting Everpure data with MCP servers for other parts of your infrastructure, so an AI assistant can correlate information across storage, virtualization, networking, observability, and other systems. Instead of becoming yet another management silo, data on Everpure storage can become part of the AI tools that IT admins are already using. Do I need an MCP server for every array? For admins managing larger environments, one attendee had a very practical question: If I have 18 arrays, do I need 18 MCP servers? No—and that answer gets to an important difference in Everpure’s approach. With Everpure Fusion, the focus is the fleet rather than individual arrays, so its MCP server can work across a fleet containing multiple FlashArray and FlashBlade systems. Configure it once rather than treating every array as a separate management island. And that extends into monitoring fleets—rather than logging into dozens of systems and assembling the picture yourself, Pure1 AI Copilot can help provide a fleet-wide health view and then let you drill into systems requiring attention. What’s next? The panel of experts discussed everything from future support for OpenStack and vCenter correlation, to extending into replication analysis and write capabilities The takeaway was simple: AI isn’t being positioned as a replacement for the storage admin. Think of it as a “high-performing teammate”: It can help find you information, investigate problems, explain unfamiliar environments, accelerate onboarding, and bring fleet-wide storage intelligence into the AI tools you already use. For IT teams continually being asked to “get more done with less”, that may be the most useful application of AI of all: spending less time managing the complexity of storage and more time managing the data that matters. Check out this and all our other Ask Us Everything sessions. And, keep the conversation going by jumping into the Everpure Community.
Fusion MCP Server Is Now Released & Open Source
There’s a narrow band between “AI demo” and “actually useful in production,” and most tools miss it by a country mile. Fusion MCP Server doesn’t. Now that it’s open source, MCP-compatible AI assistants get a controlled bridge into Everpure FlashArray and FlashBlade environments, one built to answer real operational questions about fleet inventory, capacity, performance, alerts, volumes, file systems, workloads, and presets, without turning your storage estate into a science fair project. The AI Agent doesn’t get to vibe its way through your infrastructure. It works through a clean tool surface backed by real Everpure APIs, and writes stay hidden until you flip them on yourself. What it actually solves If you’ve ever wanted to ask a storage question in plain English and get something better than a dashboard scavenger hunt, this is that. Fusion MCP Server sits between your AI agent and your arrays: the assistant talks to the server, the server authenticates with configured array API tokens, calls supported Everpure APIs, and returns structured results. Your assistant never touches the arrays directly. Practically, that means engineers can ask things like: Show me the fleet overview Which arrays have capacity concerns Show array performance for the last 24 hours List workload presets available in this fleet Show active alerts with remediation links The data was never the problem. Storage teams already have it. What eats the day is bouncing between menus, tabs, and API docs just to answer something like “which arrays are closest to full?” Think of it like swapping a pile of ad hoc curl commands and tribal knowledge for a typed interface your AI assistant can reason over. Calling it “screen scraping with confidence” undersells it. It behaves more like a junior SRE who actually reads the schema. Why engineers should care The release leads with reads, which is exactly the right default for infrastructure tooling. Out of the box, Fusion MCP Server covers fleet overview, capacity and performance, storage objects, configuration audit, and optional supervised actions for placement recommendations, preset creation and updates, and workload deployment. A few details stand out: It works with FlashArray and FlashBlade environments in a Fusion fleet, including mixed environments, as long as you provide at least one token for each platform type for Remote Execution. If the API version you have on your arrays does not yet have the endpoints with Remote Execution capability enabled, you must supply an API token for every array in the fleet. More on that in the next section. Fleet discovery covers the supported read workflows broadly, though some, especially performance, still need a direct token for each array you want to query. Built-in prompts handle fleet, performance, and config workflows, and it also works through plain natural-language questions if your agent doesn’t expose MCP prompts directly. Read-only endpoint documentation plus a whitelisted authenticated GET fetch tool cover supported API surface beyond the dedicated tools. Let’s talk Tokens first Back in Purity REST API version 2.38, Everpure started to include a capability for API endpoints called Remote Execution. This is the mechanism that lets a client invoke a Purity REST API request on a different fleet member, and that request executes as though it were initiated locally on that remote member. The catch is that both arrays must have the same API version available on them as well as the endpoint being executed against must have Remote Execution capability. As of today, not all endpoints have this capability, so there must be an API token specified for each array in the fleet until they have all been enabled. We are diligently working to get all endpoints enabled to make this easier for everyone. Stay tuned! Installation (that does not require a PhD) The setup flow is refreshingly direct: Download the latest binary from GitHub Releases or build from source. Generate API tokens for the arrays you want to query. Run generate-config with your FlashArray and/or FlashBlade targets. Drop the generated config into your MCP-capable agent using the standard start --auth-config pattern over STDIO. generate-config does more than write boilerplate. It validates tokens, detects each array’s API version, resolves array names, and writes the auth config with restrictive permissions: the config directory gets 0700 and the file gets 0600. Want an even easier way? How you just tell your AI Agent to “Read this repository at https://github.com/PureStorage-OpenConnect/fusion-mcp-server and the included USER_GUIDE.md file and add the Fusion MCP server to this agent.” Easy-peasy as it’ll step you thought the process and create the config file for you. A few caveats are worth flagging before you point this at anything that matters: The published binaries aren’t signed, so macOS and Windows may throw a warning on first run. Build from source if that’s a dealbreaker. It’s all there, have at it! Keep the generated auth-config.json local, don’t share it, and rotate tokens if one ever leaks. None of that is friction. It’s the fine print you’d want before trusting a tool with API tokens. Supervised write actions: powerful, optional, and very much not on by default Now for the part everyone asks about first, and the part some people should absolutely not enable first: write actions. Fusion MCP Server hides write tools by default. You turn them on explicitly, either during config generation with --enable-write-tools or later with update-config --enable-write-tools. Enabled, the supervised actions cover these processes with more to come as the product evolves: Placement recommendations Workload preset creation Workload preset updates Workload deployment The approval step is the clever bit. Write operations sit behind an explicit confirmation. If the agent supports MCP Elicitation, the server pops up an interactive dialog for every write tool, so you can review the proposed action and approve or decline before anything changes. If the agent doesn’t support Elicitation, it falls back to telling the agent, in plain instructions, to ask you for approval before resubmitting the call. One practical wrinkle: the write tools inherit whatever permissions live on the API tokens you configure, so the workflow only works if those tokens can perform the write. So when should you flip the switch? Enable write tools if you want supervised acceleration on repeatable workflows: placing a workload from a known preset, updating a policy-backed preset, or turning a natural-language request into a deployment action that still needs a human to sign off. Skip it if you’re still validating token scope, using the server mainly for observability, or introducing MCP to a team that hasn’t built trust in the read-only workflows yet. Start with read-only. Make it boring. Then decide whether supervised writes are the next move. Write tools toggle on and off with a simple update-config command, so this isn’t a one-way door: turn them off again anytime with update-config --enable-write-tools=false. Use cases that actually matter This release isn’t for people who like screenshots of AI chats. It’s for engineers and operators who want faster answers and safer workflows. A few obvious wins: Fleet triage Ask for a fleet overview with alerts, array inventory, Purity version, and fleet connections, the kind of first-response context you want before guessing which dashboard to open. Capacity and performance review Ask which configured arrays have the highest used capacity, which have high latency, or pull performance for the last 24 hours. For teams juggling multiple arrays, this turns routine health checks into a single conversation. Storage object lookup Query volumes by naming pattern, list file systems on FlashBlade, or inspect workloads on a specific array. Useful for anyone who inherited naming conventions from a previous geological era. Configuration audit Use the built-in documentation and read-only fetch coverage to compare settings across arrays and check for policy consistency. Handy if you’re trying to catch drift without hand-rolling an audit script every quarter. Workload lifecycle acceleration Enable supervised writes and the assistant can recommend placement, create or update presets, and deploy workloads from those presets. At that point the server stops acting like a reporting tool and starts acting like an interface layer for intent-driven operations. On My Soapbox: Why the Open Source release matters Being open source here changes the trust model, not just the distribution channel. You can inspect how the bridge works, check the security assumptions yourself, and contribute fixes instead of filing a ticket into the void. When reality disagrees with the documentation, which happens to every project sooner or later, you can open an issue instead of just living with it. The repository is public under Apache 2.0, with contribution guidance, architecture notes, a developer guide, and a dedicated issue tracker for support, issues, and feature requests. That means the people who’ll stress-test this in real environments can also be the ones fixing it. For a tool sitting between AI agents and production-adjacent storage workflows, that’s where the engineering conversation belongs. Final thought Fusion MCP Server is short on hype and long on mechanical sympathy. Read workflows stay front and center, write workflows require your explicit sign-off, and installation doesn’t eat your afternoon. If you’re running Fusion-managed fleets (which y’all should be!), it’s worth a look. Grab the latest release, point it at your MCP-capable agent, start read-only, and see how fast “show me my fleet overview” becomes second nature. It’s open source. Once you’ve kicked the tires, contribute code if you build something useful, and open an issue when you hit an edge case. That’s the whole deal.
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