No install? Use cloud.anythingmcp.com directly.
Sign in, install the BambooHR in one click, paste the credentials, mint an MCP API key — done. No Docker, no git clone, no local server to run.
TL;DR
Manage BambooHR (employees, time-off, custom reports, fields, files) from any AI agent. 9 tools, basic-auth.
💡 Keine Installation? Nutze cloud.anythingmcp.com direkt. Einloggen, Connectors → BambooHR klicken, Zugangsdaten einfügen, MCP-API-Key erzeugen — fertig. Kein Docker, kein
git clone, kein lokaler Server.
BambooHR + Gemini
Manage BambooHR (employees, time-off, custom reports, fields, files) from any AI agent. 9 tools, basic-auth.
Voraussetzungen
Die vollständige Setup-Anleitung ist in den Connector eingebaut (im Store sichtbar, wenn du den Connector auswählst). Benötigte Umgebungsvariablen für diesen Connector:
BAMBOOHR_SUBDOMAIN, BAMBOOHR_API_KEY
Schritt 1 — Zugangsdaten holen
This connector wraps the BambooHR REST API v1 (per-subdomain — api.bamboohr.com/api/gateway.php/<subdomain>/v1).
Setup:
- Sign in to your BambooHR portal → top-right avatar → API Keys → Add a new key.
- Note your subdomain (the part before
.bamboohr.com) and copy the key. - Set
BAMBOOHR_SUBDOMAINandBAMBOOHR_API_KEY.
Authentication: HTTP Basic — API key as username, literal x as password.
Path subdomain: BambooHR's API embeds the subdomain in the URL path. The adapter substitutes via runtime envVar.
…(continued in the in-app connector instructions)
Schritt 2 — Adapter installieren
git clone https://github.com/HelpCode-ai/anythingmcp.git
cd anythingmcp && docker compose up -d
Schritt 3 — Connector in Gemini hinzufügen
Gemini CLI liest MCP-Server aus ~/.gemini/settings.json (oder %APPDATA%\gemini\settings.json unter Windows). Hinzufügen:
{
"mcpServers": {
"anythingmcp": {
"httpUrl": "https://cloud.anythingmcp.com/mcp",
"headers": { "Authorization": "Bearer YOUR_MCP_API_KEY" }
}
}
}
- Hole deinen MCP API Key aus AnythingMCP → Profil → MCP API Keys → Neuer Key.
- Speichern und
geminineu starten. /mcpin Gemini CLI ausführen —BambooHRsollte als verfügbar gelistet sein.- Vertex AI Studio:
https://cloud.anythingmcp.com/mcpimtools-Array der Anfrage mit demselben Bearer-Header übergeben.
Verfügbare Tools
| Tool | What it does |
|---|---|
bamboohr_list_employees_directory | Returns the company directory: every employee's id, displayName, jobTitle, workEmail, department, division, location, supervisor |
bamboohr_get_employee | Get one employee by ID |
bamboohr_add_employee | Create a new employee |
bamboohr_update_employee | Update employee fields |
bamboohr_who_is_out | Returns the company-wide who's-out list for a date range |
bamboohr_list_time_off_requests | List time-off requests |
bamboohr_list_meta_fields | List every field defined in this account (built-in + custom) |
bamboohr_run_custom_report | Run a pre-defined custom report by its numeric ID |
bamboohr_get_employee_files | List files in an employee's record categorized by section |
FAQ
Unterstützen Gemini 1.5 Pro oder 2.x MCP? Ja — Gemini CLI ≥ 0.4 und die Vertex AI Tools API akzeptieren MCP httpUrl-Connectors mit Bearer-Header.
Nächste Schritte
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