A Model Context Protocol server for PNLCS, the open-source billing panel. Connect Claude Code, Claude Desktop, Cursor, VS Code or any other MCP client to your PNLCS install and work with it in plain English:

"Which invoices are overdue?" — "Show me this client's services and domains." — "Any orders held as fraud today?" — "Open a ticket for ada@example.com about her domain renewal."

Zero dependencies. The server is one small Node process that talks to the PNLCS admin API you already have. Nothing is installed on the PNLCS side.

• npm: pnlcs-mcp
• Requires: Node 18+ on the machine your AI client runs on
• Transport: stdio (spawned by your client — no port, nothing to host)

1. Log in to the admin area of your PNLCS install.
2. Go to Configuration → API Credentials.
3. Click Create, give it a name like mcp, and copy the two values it shows you:
   • Identifier — the credential's username
   • Secret — shown once; store it somewhere safe

That pair is all the server needs, passed through three environment variables:

One command:

claude mcp add pnlcs \
  --env PNLCS_URL=https://billing.example.com \
  --env PNLCS_IDENTIFIER=your_identifier \
  --env PNLCS_SECRET=your_secret \
  -- npx -y pnlcs-mcp

Add --env PNLCS_ALLOW_WRITES=1 if you also want the write tools. Use --scope project to share the entry with your team via .mcp.json (put the secret in your shell environment, not in the committed file). Check it with claude mcp list; remove it with claude mcp remove pnlcs.

Edit claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):

{
  "mcpServers": {
    "pnlcs": {
      "command": "npx",
      "args": ["-y", "pnlcs-mcp"],
      "env": {
        "PNLCS_URL": "https://billing.example.com",
        "PNLCS_IDENTIFIER": "your_identifier",
        "PNLCS_SECRET": "your_secret"
      }
    }
  }
}

Restart Claude Desktop; the tools appear under the hammer icon.

Settings → MCP → Add new global MCP server, or create .cursor/mcp.json in your project (same JSON shape as Claude Desktop above).

Create .vscode/mcp.json:

{
  "servers": {
    "pnlcs": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "pnlcs-mcp"],
      "env": {
        "PNLCS_URL": "https://billing.example.com",
        "PNLCS_IDENTIFIER": "your_identifier",
        "PNLCS_SECRET": "your_secret"
      }
    }
  }
}

Every MCP client that can spawn a stdio server uses the same three pieces: command npx, args ["-y", "pnlcs-mcp"], and the environment variables above. From a git checkout, node mcp/server.js works identically.

Without the flag the write tools are not merely hidden — calling one is refused before any HTTP happens. An assistant wired up for reporting cannot even see a suspend button. Give a reporting setup a read-only life by simply not setting the flag.

• The secret only ever travels between the machine running your AI client and your PNLCS install, over the same HTTPS API your admin screens use. It is never sent to the model provider; the model sees tool results.
• Prefer a dedicated API credential for MCP so you can revoke it alone.
• PNLCS rate-limits API credentials (300 requests/minute per credential), so a runaway agent cannot hammer your install.
• Keep PNLCS_ALLOW_WRITES off unless you actually want the assistant acting on your books, and read what it proposes before approving tool calls that create or change things.

npm test runs the offline suite: it spawns the real server process, speaks the real protocol to it, and checks every tool's HTTP shape against a local stub — plus the write-gate in both directions, batch requests from older protocol revisions, version negotiation, timeouts and shutdown draining.

node test/live.mjs runs every tool against a real install — point it at a demo, never at production books; it creates clearly-named disposable data for the write tools and prints one PASS/FAIL line per tool.