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Greenlake Subscriptions MCP

by HewlettPackardversion 1.1.2i★ 9iRuns on your computer
Registry nameio.github.HewlettPackard/greenlake-subscriptions-mcp

Install

Overview

Description from the maker (original English)

HPE GreenLake subscriptions MCP Server

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 2 of 2 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "greenlake-subscriptions": {
      "command": "uvx",
      "args": [
        "greenlake-subscriptions-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add greenlake-subscriptions -- uvx greenlake-subscriptions-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "greenlake-subscriptions": {
      "command": "uvx",
      "args": [
        "greenlake-subscriptions-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "greenlake-subscriptions": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "greenlake-subscriptions-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 10, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account HewlettPackard, which owns the repo

  • ✓ Pass

    Still maintained

    Code last changed 6 months ago

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 95% of the other 19082 servers in this category
  • Published to the registry more recently than 8% of the other 19082 servers in this category
  • Basic checks equal to or better than 100% of the other 19082 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Greenlake Subscriptions MCP safe?

It passed 2 of 2 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed 6 months ago, and the latest version (1.1.2) reached the registry 6 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: World Monitor, Claude Flow, Tldraw

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

World Monitor

Real-time global intelligence on conflict, markets, energy, trade, shipping and cyber. 102 tools.

by koala73Widely used★ 90.4KUpdated 5 days ago

Runs in the cloud✓ worldmonitor.app

Claude Flow

AI orchestration with hive-mind swarms, neural networks, and 87 MCP tools for enterprise dev.

by ruvnetWidely used↓ 13.5K/wk★ 81.4KUpdated 14 months ago

Runs on your computer

Tldraw

Draw and visually collaborate with your agents on tldraw's canvas.

by tldrawWidely used★ 51.4KUpdated 8 months ago

Runs in the cloud

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

HPE GreenLake MCP Servers

A collection of Model Context Protocol (MCP) servers that enable AI assistants to interact with HPE GreenLake platform services.

Table of Contents

  • What is the Model Context Protocol?
  • What's in this Repository?
  • Available MCP Servers
  • Quick Start
  • MCP Client Configuration
  • Tool Modes
  • Architecture Overview
  • Security Considerations
  • Troubleshooting
  • Getting Help
  • Resources
  • License

What is the Model Context Protocol?

The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need.

What's in this Repository?

This repository contains production-ready MCP servers for HPE GreenLake platform services. Each server provides AI assistants with secure, read-only access to specific GreenLake APIs, enabling natural language interactions with your HPE infrastructure.

Key Features
  • Standardized Architecture: All servers follow consistent patterns for authentication, configuration, and error handling
  • OAuth2 Authentication: Secure access using HPE GreenLake workspace credentials with automatic token management
  • Dual Tool Modes: Each server supports both static (individual tools per endpoint) and dynamic (meta-tools) operation modes
  • Type-Safe: Built with Pydantic models for runtime validation and type safety
  • Production Ready: Comprehensive logging, error handling, and Docker support
  • Well Tested: Extensive unit and integration test coverage

Available MCP Servers

ServiceCategoryDescriptionKey Capabilities
DevicesInfrastructure & PlatformManage and query HPE GreenLake devices in your workspaceFilter by type, serial number, tags; retrieve device details by ID
WorkspacesInfrastructure & PlatformInteract with HPE GreenLake workspace management APIsList/query workspaces; retrieve details and configurations
SubscriptionsInfrastructure & PlatformAccess subscription information and licensing detailsView active subscriptions; query licensing; monitor subscription status
Audit LogsSecurity & ComplianceQuery and analyze HPE GreenLake audit logsFilter by category, user, timestamp; track user activities and system events
UsersUser & Access ManagementManage user accounts and access controlsList/query users; view permissions; monitor user activity
ReportingReporting & AnalyticsGenerate and retrieve reports on workspaces, devices, and subscriptionsCustomizable report types; async report tracking; date range and column filters
Service CatalogInfrastructure & PlatformAccess service catalog and provisioning informationView service offers, provisions, managers, manager-provisions and regions

Quick Start

Prerequisites
  • Python 3.10 or higher
  • uv package manager
  • HPE GreenLake workspace with API credentials
Configuration

All servers require the following HPE GreenLake credentials:

export GREENLAKE_API_BASE_URL="https://global.api.greenlake.hpe.com"

export GREENLAKE_CLIENT_ID="your-client-id" export GREENLAKE_CLIENT_SECRET="your-client-secret" export GREENLAKE_WORKSPACE_ID="your-workspace-id"

Running a Server
# Navigate to a server directory

cd src/audit-logs

Install dependencies

uv sync

Run the server

uv run python -m greenlake_audit_logs_mcp

MCP Client Configuration

Claude Desktop

Add servers to your claude_desktop_config.json:

{

"mcpServers": { "greenlake-audit-logs": { "command": "uv", "args": ["run", "python", "-m", "greenlake_audit_logs_mcp"], "cwd": "/path/to/gl-mcp/src/audit-logs", "env": { "GREENLAKE_API_BASE_URL": "https://global.api.greenlake.hpe.com", "GREENLAKE_CLIENT_ID": "your-client-id", "GREENLAKE_CLIENT_SECRET": "your-client-secret", "GREENLAKE_WORKSPACE_ID": "your-workspace-id" } }, "greenlake-devices": { "command": "uv", "args": ["run", "python", "-m", "greenlake_devices_mcp"], "cwd": "/path/to/gl-mcp/src/devices", "env": { "GREENLAKE_API_BASE_URL": "https://global.api.greenlake.hpe.com", "GREENLAKE_CLIENT_ID": "your-client-id", "GREENLAKE_CLIENT_SECRET": "your-client-secret", "GREENLAKE_WORKSPACE_ID": "your-workspace-id" } } } }

Other MCP Clients

These servers use the standard stdio transport and work with any MCP-compatible client. See individual server READMEs for specific configuration examples.

Tool Modes

All servers support two operation modes that can be switched at runtime using the MCP_TOOL_MODE environment variable:

Static Mode (Default)
  • Individual tools: Each API endpoint becomes a dedicated MCP tool
  • Type-safe: Explicit tool definitions with compile-time validation
  • Discoverable: Tools appear individually in MCP client interfaces
  • Best for: Smaller APIs and focused workflows
export MCP_TOOL_MODE=static
Dynamic Mode
  • Meta-tools: 3 generic tools that can handle any API endpoint
  • list_endpoints - Discover available API endpoints
  • get_endpoint_schema - Get detailed schema information
  • invoke_dynamic_tool - Execute API calls with runtime validation
  • Memory efficient: Lower overhead for large APIs
  • Best for: Large APIs with many endpoints
export MCP_TOOL_MODE=dynamic
Compatibility Matrix
Python VersionStatusNotes
3.12✅ SupportedRecommended
3.11✅ SupportedRecommended
3.10✅ SupportedMinimum version

Architecture Overview

High-Level Architecture
flowchart TB

subgraph clients[" MCP Clients "] direction TB claude["Claude Desktop"] vscode["VS Code"] other["..."] end

subgraph servers[" MCP Servers "] direction TB audit["Audit Logs"] devices["Devices"] users["Users"] workspaces["Workspaces"] subscriptions["Subscriptions"] reporting["Reporting"] serviceX["..."]

subgraph shared[" Shared Components "] direction LR oauth["OAuth2 Token Manager (with caching)"] end end

subgraph platform[" HPE GreenLake Platform "] direction TB api["APIs"] end

clients ==>|JSON-RPC/stdio| servers servers ==>|HTTPS/REST + OAuth2| platform

classDef clientStyle fill:#2196F3,stroke:none,color:#fff,rx:10,ry:10 classDef serverStyle fill:#4CAF50,stroke:none,color:#fff,rx:10,ry:10 classDef authStyle fill:#FF9800,stroke:none,color:#fff,rx:10,ry:10 classDef platformStyle fill:#01a982,stroke:none,color:#fff,rx:10,ry:10 classDef clientContainerStyle fill:none,stroke:#2196F3,stroke-width:3px,rx:15,ry:15 classDef serverContainerStyle fill:none,stroke:#4CAF50,stroke-width:3px,rx:15,ry:15 classDef authContainerStyle fill:none,stroke:#FF9800,stroke-width:2px,stroke-dasharray:5 5,rx:10,ry:10 classDef platformContainerStyle fill:none,stroke:#01a982,stroke-width:3px,rx:15,ry:15

class claude,vscode,other clientStyle class audit,devices,users,workspaces,subscriptions,reporting,serviceX serverStyle class oauth authStyle class api platformStyle class clients clientContainerStyle class servers serverContainerStyle class shared authContainerStyle class platform platformContainerStyle

Request Flow
  1. Client Request: MCP client sends tool invocation via JSON-RPC over stdio
  2. Authentication: Server authenticates with HPE GreenLake using OAuth2 (tokens cached and auto-refreshed)
  3. API Call: Server makes authenticated HTTPS request to GreenLake API
  4. Response: Server processes response and returns data to client via MCP protocol
Shared Components

All servers share a common architecture:

src/<service>/

├── greenlake_ _mcp/ # Main package (e.g. greenlake_audit_logs_mcp) │ ├── main.py # Entry point (python -m greenlake_ _mcp) │ ├── auth/ # OAuth2 authentication │ ├── config/ # Settings and logging │ ├── models/ # Pydantic data models │ ├── server/ # MCP server core │ ├── tools/ # Tool implementations │ │ ├── base.py # Base tool class │ │ ├── registry.py # Tool registration │ │ └── implementations/ # Tool implementations │ └── utils/ # HTTP client and utilities ├── tests/ # Unit and integration tests └── pyproject.toml # Package definition and entry points

Authentication

All servers use OAuth2 client credentials flow with automatic token management:

  • Tokens are cached and automatically refreshed
  • Thread-safe token management
  • Configurable retry logic with exponential backoff
Logging

Servers use structured logging with MCP protocol compliance:

  • stderr: Diagnostic logs (controlled by GREENLAKE_LOG_LEVEL)
  • File logging: Optional detailed logging to ~/.hpe/mcp-logs/
  • stdout: Reserved for JSON-RPC messages only

Security Considerations

  • Read-Only Access: All servers provide read-only API access by design
  • Credential Management: Never commit credentials to version control
  • Token Security: OAuth2 tokens are cached in memory only
  • Workspace Isolation: Operations are scoped to the configured workspace
  • HTTPS Only: All API communications use TLS encryption

Troubleshooting

Common Issues

Server won't start:

  • Verify all required environment variables are set
  • Check that uv is installed and dependencies are synced
  • Review stderr output for configuration errors

Authentication failures:

  • Verify client credentials are valid for your workspace
  • Ensure GREENLAKE_WORKSPACE_ID matches your credential scope
  • Check network connectivity to GreenLake APIs

MCP client connection issues:

  • Verify the cwd path in client configuration is correct
  • Check that the server starts successfully when run manually
  • Review client logs for specific error messages
Debug Logging

Enable detailed logging for troubleshooting:

export GREENLAKE_LOG_LEVEL=DEBUG

export GREENLAKE_FILE_LOGGING=true

Log files are written to: ~/.hpe/mcp-logs/{service-name}/

Getting Help

Gi

Only the beginning is shown; read the rest in the repo

Ask PopNx