Supabase
Supabase’s official server lets AI create tables, query data and review project settings in your Supabase database.
Description from the maker (original English)
Every Level RMM endpoint, plus a local SQLite fleet store and offline cross-entity rollups no Level
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
LEVELIO_BASE_URL, LEVEL_API_TOKENValues marked secret are keys to your account. Never share them or paste them into a chat.
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Download the maker’s .mcpb file
The link goes to the maker’s own file; we do not host or repackage it. Open the file and Claude Desktop installs it.
Run this in your terminal
Fill in the empty values before running it.
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.
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.
LEVELIO_BASE_URL envThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
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 7, 2026
The registry name was verified with the GitHub account servosity, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 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.
You need to fill in: LEVEL_API_TOKEN
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).
The GitHub repo last changed –, and the latest version (0.1.8) reached the registry 14 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need MCPB on your computer.
In the same category: Supabase, MongoDB MCP Server, Elasticsearch MCP Server
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Supabase’s official server lets AI create tables, query data and review project settings in your Supabase database.
MongoDB Model Context Protocol Server
MCP server for interacting with Elasticsearch
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.
Unofficial. Community-built Claude Code Skill and MCP server for the Level
API. Not affiliated with, endorsed by, or sponsored by Level.
▶ Watch the 30-second demo - demo data is simulated; every command shown exists in the real CLI.
Every Level RMM endpoint, plus a local SQLite fleet store and offline cross-entity rollups no Level tool has: at-risk ranking, patch posture, alert triage, and stale-device detection in one command. Works with the AI you already use - ChatGPT (desktop app), Claude Desktop, Codex, Claude Code, Claude Cowork, and GitHub Copilot - plus Gemini CLI. Free, open source, runs on your laptop. Built for MSP owners. No code required.
Security note (until this connector is reprinted). Do not put this
connector behind the
supergatewaybridge or any other stdio-to-HTTP bridge,
not even on localhost. The bridge adds no password, so anyone who can reach
its port can use the MCP tools with your credentials. Use the default stdio
transport. If you set up a bridge or a tunnel for a remote agent, shut it
down. A fix is on the way.
The six agents MSP owners actually use (self-serve, works today):
| Your AI agent | How to install the Level skill |
|---|---|
| Claude Desktop | Run installer, then Settings > Extensions to register levelio-mcp (no JSON editing). |
| ChatGPT (desktop app) | Run installer, then add levelio-mcp in Settings > MCP servers as a local (STDIO) server. ChatGPT on the web is not supported yet. |
| Codex CLI | Paste the install prompt below. |
| Claude Code | Paste the install prompt below. |
| Claude Cowork | Paste the install prompt below. |
| GitHub Copilot (VS Code) | Run installer, add levelio-mcp to mcp.json under the servers key, then pick Agent mode. |
The ChatGPT desktop app runs levelio-mcp locally and shares MCP configuration with Codex (OpenAI). ChatGPT on the web needs a remote (HTTP) server. This connector speaks stdio only, and the bridge it would need is paused until it is reprinted (see the security note in mcp-install.md), so msp-skills does not support it yet. If your team works there, a hosted gateway is the better pick; check that your AI app is on the gateway's setup list.
Big install base, but an honest heads-up: Gemini CLI runs the local binary you just installed, while Microsoft 365 Copilot and the Gemini app need a remote server, which msp-skills does not support yet.
| Agent | What it takes |
|---|---|
| Microsoft 365 Copilot / Copilot Studio | Not supported by msp-skills yet: it needs a remote server. A hosted gateway is the better pick; check that your AI app is on the gateway's setup list. |
| Google Gemini | Gemini CLI is local - same as Claude Code. See mcp-install.md. The Gemini app needs a remote server and is not supported by msp-skills yet. |
Skill-native agents (also covered): Hermes and OpenClaw read this skill's
SKILL.mddirectly *and* speak MCP - see their install sections below. Also works with Cursor, Windsurf, Cline, Continue.dev, and Zed via MCP. Full per-tool wire-up: [docs/which-agent.md](../../docs/which-agent.md).
Run more than one agent? Install across all 51+ supported agents in one command:
npx skills add Servosity/msp-skills@latest(requires Node.js, then run the per-skill installer for the CLI/MCP binaries). See docs/which-agent.md.
.mcpbClaude Desktop bundle: the
.mcpblaunches on macOS (Intel and Apple Silicon), Windows x64 and Linux x64. Every tool shells out to the companionlevelio-cli. Releases cut from 2026-09-18 on ship that CLI inside the bundle; older bundles contain only the MCP server, so if yours lacks the companion, run the Path A or Path B installer below first (or setLEVELIO_CLI_PATHto an existing binary). Details: #331.
Download Level MCP (.mcpb) - then open Claude Desktop > Settings > Extensions and select the file. One click, no JSON, no shell. (Browse every Level release on the releases page.)
Prefer the Claude Code plugin? Add the marketplace once, then install - works immediately, no directory listing required:
/plugin marketplace add Servosity/msp-skills
/plugin install levelio@msp-skills
Copy this into Claude Code, Codex CLI, or Claude Cowork:
Install the Level Skill and MCP server from Servosity/msp-skills in this agent workspace. If this workspace uses a POSIX shell (macOS, Linux, WSL, or Bash), run
bash <(curl -fsSL https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/levelio/install.sh). If it uses Windows PowerShell, runiwr -useb https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/levelio/install.ps1 | iex. Then authenticate per the README and runlevelio-cli --helpto explore.
The same prompt works in any agent that can run shell.
Windows (PowerShell):
iwr -useb https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/levelio/install.ps1 | iex
macOS / Linux:
bash <(curl -fsSL https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/levelio/install.sh)
The installer drops both levelio-cli (the CLI) and levelio-mcp (the MCP server) into your user bin path. Claude Code, Codex, and Cowork discover the Skill via SKILL.md in this directory.
Verify:
levelio-cli --version
The installer always fetches the current release - re-run it to upgrade:
macOS / Linux:
bash <(curl -fsSL https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/levelio/install.sh)
Windows (PowerShell):
iwr -useb https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/levelio/install.ps1 | iex
Claude Desktop .mcpb users: download the latest .mcpb (top of this section) and re-select it in Settings > Extensions. Claude Code plugin users: /plugin update levelio@msp-skills.
After the installer runs, see [mcp-install.md](./mcp-install.md) and [docs/which-agent.md](../../docs/which-agent.md) for the per-agent wire-up - one section per agent, including the GitHub Copilot servers key and the ChatGPT desktop app. Claude Desktop's Settings > Extensions panel is the simplest path; the MCP config block (for users who prefer editing JSON) is documented in mcp-install.md.
From the Hermes CLI:
hermes skills install servosity/msp-skills/skills/levelio --force
Inside a Hermes chat session:
/skills install servosity/msp-skills/skills/levelio --force
Hermes speaks MCP natively, so it can also use the levelio-mcp server directly - same install path, same env vars.
Tell your OpenClaw agent (copy this):
Install the levelio skill from https://github.com/servosity/msp-skills/tree/main/skills/levelio. The skill defines how its required CLI (
levelio-cli) can be installed via theopenclaw:frontmatter block.
OpenClaw isn't generally available yet; the frontmatter wiring is pre-shipped and will activate the moment OpenClaw launches.
Set the credentials the CLI needs (from your Level portal):
LEVEL_API_TOKEN=<value> levelio-cli doctor
doctor confirms the credentials work before you run anything that touches data. It exits 0 even when the credential is rejected, so scripts should add --fail-on error.
| Question your MSP keeps asking | Command |
|---|---|
| Which devices are most at risk across alerts, patches, score, and staleness? | levelio-cli at-risk --top 20 |
| Which devices have gone dark and stopped checking in? | levelio-cli stale --days 30 |
| What is my fleet-wide patch exposure, by category? | levelio-cli patch-posture --category security |
| How is my fleet broken down by OS, platform, or group? | levelio-cli fleet --by os |
| Where are my active critical fires, clustered by group? | levelio-cli alert-triage --severity critical |
| Give me a per-client posture scorecard for QBRs. | levelio-cli client-scorecard |
| Which devices are below my security-score threshold? | levelio-cli security-posture --below 70 |
| Which devices are waiting on a reboot to finish patching? | levelio-cli reboot-due |
| Which monitors fire most often across the fleet? | levelio-cli alert-recurrence --top 15 |
| What changed since yesterday - new alerts, updates, activity? | levelio-cli since --days 1 |
Full command reference: guide.md. For the AI-agent operating contract (--agent, --dry-run, when to confirm before mutating), see AGENTS.md.
Most Level integrations and MCP servers proxy each question into a live API call. That's fine for one record. It dies at scale, when you're asking "which of my 600 endpoints across every client need attention first" or "what's each client's patch exposure for the QBR deck."
This skill syncs Level into a local SQLite mirror with full-text search. Aggregate questions become one local SQL join: instant, offline, and the AI sees the answer, not the raw data. Compound commands like at-risk, client-scorecard, and cf-coverage join across devices, alerts, OS updates, groups, and custom fields - work a stateless API wrapper can't do.
Level is a fast, modern RMM, but like every RMM its console answers questions one device at a time. The recurring complaint on r/msp isn't monitoring - it's *reporting across the whole fleet at once*: which machines quietly went dark, which clients are furthest behind on patches, where the alert noise is systemic, and what each client's posture is when a QBR is due. There's no single screen for that, so the numbers that matter get assembled by hand, group tab by group tab.
This skill closes that gap with cross-entity rollups:
Only the beginning is shown; read the rest in the repo