Kaseya BMS MCP

Kaseya BMS + AI - for Claude, the ChatGPT desktop app, GitHub Copilot, Gemini CLI, and any agent that speaks MCP

Unofficial. Community-built Claude Code Skill and MCP server for the Kaseya BMS API. Not affiliated with, endorsed by, or sponsored by Kaseya US LLC.

[Kaseya BMS demo - animated preview]

_{▶ Watch the 30-second demo - demo data is simulated; every command shown exists in the real CLI.}

The first dedicated CLI and MCP server for Kaseya BMS - the full PSA surface plus offline sync, full-text search, and the queue, contract-burn, and unbilled-time analytics the web grid can't compute. 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.

Works with your agent

Security note (until this connector is reprinted). Do not run HTTP mode (--transport http), not even on localhost. This build has no password, listens on every network interface, and predates the browser-rebinding guard, so a web page you open can reach it. The MCP tools can also make changes with your credentials. Use the default stdio transport. If you set up 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):

The ChatGPT desktop app runs kaseya-bms-mcp locally and shares MCP configuration with Codex (OpenAI). ChatGPT on the web needs a remote (HTTP) server, and this connector's HTTP mode 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.

Also for the Microsoft and Google stacks

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.

Skill-native agents (also covered): Hermes and OpenClaw read this skill's SKILL.md directly 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.

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.

Install in 60 seconds

Fastest for Claude Desktop - one-click .mcpb

Claude Desktop bundle: the .mcpb launches on macOS (Intel and Apple Silicon), Windows x64 and Linux x64. Every tool shells out to the companion kaseya-bms-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 set KASEYA_BMS_CLI_PATH to an existing binary). Details: #331.

Download Kaseya BMS MCP (.mcpb) - then open Claude Desktop > Settings > Extensions and select the file. One click, no JSON, no shell. (Browse every Kaseya BMS 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 kaseya-bms@msp-skills

Path A - paste one prompt into your AI agent (recommended)

Copy this into Claude Code, Codex CLI, or Claude Cowork:

Install the Kaseya BMS 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/kaseya-bms/install.sh). If it uses Windows PowerShell, run iwr -useb https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/kaseya-bms/install.ps1 | iex. Then authenticate per the README and run kaseya-bms-cli --help to explore.

The same prompt works in any agent that can run shell.

Path B - run the installer yourself

Windows (PowerShell):

iwr -useb https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/kaseya-bms/install.ps1 | iex

macOS / Linux:

bash <(curl -fsSL https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/kaseya-bms/install.sh)

The installer drops both kaseya-bms-cli (the CLI) and kaseya-bms-mcp (the MCP server) into your user bin path. Claude Code, Codex, and Cowork discover the Skill via SKILL.md in this directory.

Verify:

kaseya-bms-cli --version

Upgrade to the latest 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/kaseya-bms/install.sh)

Windows (PowerShell):

iwr -useb https://raw.githubusercontent.com/Servosity/msp-skills/main/skills/kaseya-bms/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 kaseya-bms@msp-skills.

Add to Claude Desktop, the ChatGPT desktop app, GitHub Copilot, Gemini CLI, or another MCP client

After the installer runs, see mcp-install.md and 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.

Install for Hermes

From the Hermes CLI:

hermes skills install servosity/msp-skills/skills/kaseya-bms --force

Inside a Hermes chat session:

/skills install servosity/msp-skills/skills/kaseya-bms --force

Hermes speaks MCP natively, so it can also use the kaseya-bms-mcp server directly - same install path, same env vars.

Install for OpenClaw

Tell your OpenClaw agent (copy this):

Install the kaseya-bms skill from https://github.com/servosity/msp-skills/tree/main/skills/kaseya-bms. The skill defines how its required CLI (kaseya-bms-cli) can be installed via the openclaw: frontmatter block.

OpenClaw isn't generally available yet; the frontmatter wiring is pre-shipped and will activate the moment OpenClaw launches.

Authenticate

Kaseya BMS issues short-lived JWTs. The simplest path is to mint one from your BMS API-user credentials and let the CLI store it:

export KASEYA_BMS_USERNAME="[email protected]"export KASEYA_BMS_PASSWORD="<password>"   # env-only; never passed on the command lineexport KASEYA_BMS_TENANT="yourmsp"        # your company/tenant name from My Settingskaseya-bms-cli auth login                 # exchanges these for a JWT and saves it

Already have a JWT? Skip the login and pass it directly instead:

KASEYA_BMS_TOKEN=<jwt> kaseya-bms-cli doctor

Non-US tenant? Point at your region with KASEYA_BMS_BASE_URL (default https://api.bms.kaseya.com). doctor confirms the credentials work before you run anything that touches data; when commands start returning a 401 Security Error, run kaseya-bms-cli auth login again to refresh the token. It exits 0 even when the credential is rejected, so scripts should add --fail-on error.

What this skill does

Beyond these, the CLI covers the full BMS surface - 472 commands across the service desk, CRM, contracts, finance, projects, inventory, and integrations.

Full command reference: guide.md. For the AI-agent operating contract (--agent, --dry-run, when to confirm before mutating), see AGENTS.md.

What makes this different

Most Kaseya BMS 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 "how many approved billable hours are unbilled across every client this month" or "which contracts are most burned down heading into renewal" - and the BMS API's 500-requests-per-hour-per-endpoint limit punishes you for asking it live.

This skill syncs Kaseya BMS 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 workload (open tickets joined with hours logged per technician) and contract-burn (time logs routed through their tickets' contracts) join across tickets, time logs, and contracts - work a stateless API wrapper can't do.

The pain this closes

Ask any MSP running Kaseya BMS what they think of the reporting, and the answer on r/msp and in the MSPGeek community is consistent: it's the weak spot. The questions a service desk asks every morning - which queues are underwater, which tickets have gone stale, who's buried and who can take the next escalation - are an export to Excel and a pivot table, not a click. Month-end is the same chore: pulling approved-but-unbilled time per client for the invoice run, and seeing how much of a prepaid contract a client has burned before the renewal call.

This skill turns those into one command each, answered from the local mirror:

• kaseya-bms-cli queue-health --agent - open ticket volume by queue, priority, and status, with stale counts flagged before standup.
• kaseya-bms-cli stale-tickets --days 7 --agent - the specific open tickets nobody has touched in a week, oldest first.
• kaseya-bms-cli workload --agent - open load per technician joined with hours logged, so you can balance the next dispatch.
• kaseya-bms-cli unbilled --agent - approved, billable, not-yet-billed time grouped by account, in hours - the ready-to-bill review.
• kaseya-bms-cli contract-burn --window-days 90 --agent - hours consumed and percent of period elapsed per contract, at-risk first.

See pain-point.md for the longer narrative.

Frequently asked questions

Does this work with ChatGPT?

Yes, in the ChatGPT desktop app. It runs MCP servers locally (STDIO), the way kaseya-bms-mcp runs, and shares MCP configuration with Codex (OpenAI): add kaseya-bms-mcp under Settings > MCP servers as a STDIO server, or add it once to Codex's ~/.codex/config.toml. ChatGPT on the web is not supported by msp-skills yet: it needs a remote (HTTP) server, and this connector's HTTP mode is paused until it is reprinted; the security note in mcp-install.md explains why. If your team works there, a hosted gateway is the better pick; check that your AI app is on the gateway's setup list.

Does this work with Codex, Cursor, Windsurf, Cline, Copilot, or Gemini?

Yes - all of them speak MCP. Cross-tool install commands are in the matrix above and the deep-dive in docs/which-agent.md.

Do I need to know how to code?

No. The recommended install is to paste one sentence into Claude Code or Codex - your agent reads SKILL.md and does the install. The fallback is a one-line installer per OS (bash or PowerShell). Neither path requires writing code. You'll enter your Kaseya BMS credentials once.

Is my Kaseya BMS data safe?

Your data stays on your machine. The CLI and MCP server are local binaries. The SQLite mirror sits in a directory under your user account. The AI agent only sees what the CLI returns - typically a query result, not raw bulk data. Credentials are read from your environment or your agent's config; never bundled into this repo or transmitted anywhere by MSP Skills.

Will this hit my Kaseya BMS API rate limits?

Rarely. BMS allows 500 requests per hour per endpoint. Reads default to the local SQLite mirror (--data-source auto), so day-to-day questions cost zero API calls; you only spend the budget when you sync or explicitly query live with --data-source live.

Do I need to be a Kaseya customer, and how do I authenticate?

Yes - you need a Kaseya BMS tenant and an API user. The skill authenticates as that user with kaseya-bms-cli auth login (your BMS username, password, and tenant name), or you can paste a pre-minted JWT via KASEYA_BMS_TOKEN. It can only ever do what that BMS user is permitted to do.

Will this replace my Kaseya BMS portal?

No. It's a faster path for the questions you ask every day - queue health, unbilled time, pipeline - and for letting an AI agent drive the service desk. The BMS portal stays your system of record.

What does it cost?

Free. Apache-2.0 licensed. You pay only for whichever AI agent you use (Claude, ChatGPT, Codex, etc.), and that's billed by your AI provider, not by us.

Safety model

The mcp:read-only annotation on every GET command lets MCP clients gate writes automatically; the routine-write tier covers every POST/PUT/PATCH plus the verb-less import. The strongest control is the scope you grant the Kaseya BMS credentials - the CLI can only do what the credentials are permitted to do. Full details, including how to lock it down, are in governance.md.

Status

Beta. Validated against the Kaseya BMS API surface and being validated with MSPs running it live against their own production tenant in our weekly Build Sessions. RSVP at compoundingteams.com/build-sessions.

Standards. Conforms to the open Agent Skills spec (Anthropic, Dec 2025; 40+ agents). MCP-compatible - works with any MCP-capable agent including Hermes. OpenClaw-ready (frontmatter pre-wired, awaiting OpenClaw launch).

Maintained by Servosity. Apache-2.0 licensed. Built with CLI Printing Press. Last updated: 2026-06-07.