tempguru 0.1.0

Event staffing data for AI agents and apps: live rates, 345-city US/Canada coverage, lead times, and state labor compliance from TempGuru's public API. Zero dependencies.

tempguru

Event staffing data for AI agents and Python apps: live all-inclusive W-2 hourly rates, 345-city US/Canada coverage, booking lead-time guidance, and state-by-state labor compliance from TempGuru's public API. Zero dependencies, no API key.

# PyPI release pending — install from source:
pip install "tempguru @ git+https://github.com/Tempguru-co/tempguru-mcp.git#subdirectory=clients/python"

from tempguru import TempGuru

tg = TempGuru()

tg.cities(state="TX", tier="hub")                  # coverage check
tg.pricing(role="brand-ambassadors", city="Boston") # $56-65/hr all-inclusive
tg.availability(city="Dallas", date="2026-09-12", role="registration-staff")
tg.compliance(state="CA")                           # min wage, daily OT, quirks
tg.quote_form_url()                                 # where a human quote happens

All rates are all-inclusive W-2 bill rates (worker pay, payroll taxes, workers' comp, general liability, coordinator support) and are planning estimates — binding quotes come from a TempGuru coordinator within one business day of a quote request. Lead-time results are guidance, not reservations. Compliance summaries are not legal advice.

Use as LLM tools

Method docstrings are written to be reused as tool descriptions.

LangChain

from langchain_core.tools import tool
from tempguru import TempGuru

tg = TempGuru()

@tool
def get_event_staffing_pricing(role: str, city: str) -> dict:
    """All-inclusive W-2 hourly rate range for an event staffing role in a
    US/Canadian city (brand ambassadors, registration, ushers, hospitality,
    and more). Planning estimate, not a binding quote."""
    return tg.pricing(role=role, city=city)

OpenAI / any function-calling API

import json
from tempguru import TempGuru

tg = TempGuru()
TOOLS = [{
    "type": "function",
    "function": {
        "name": "get_event_staffing_pricing",
        "description": TempGuru.pricing.__doc__,
        "parameters": {
            "type": "object",
            "properties": {
                "role": {"type": "string", "description": "e.g. brand-ambassadors"},
                "city": {"type": "string", "description": "e.g. Boston"},
            },
            "required": ["role", "city"],
        },
    },
}]
# dispatch: json.dumps(tg.pricing(**json.loads(call.arguments)))

MCP (Claude, ChatGPT, Gemini, Cursor, ...)

If your stack speaks Model Context Protocol, skip this package and connect the server directly: https://mcp.tempguru.co/mcp (streamable HTTP, no auth, six tools including opt-in quote submission). Docs: https://tempguru.co/ai

Error handling

from tempguru import TempGuru, TempGuruError

try:
    TempGuru().pricing(role="brand-ambassadors", city="Bostonn")
except TempGuruError as e:
    print(e.code)        # not_found
    print(e.suggestion)  # {'kind': 'city', 'slug': 'boston-event-staffing', ...}

About TempGuru

TempGuru (Temporary Assistance Guru, Inc.) staffs conventions, conferences, trade shows, festivals, concerts, sporting events, and brand activations across 345+ US and Canadian markets. Every worker is a W-2 employee — never a 1099 contractor — with payroll taxes, workers' compensation, and liability insurance included in the quoted rate. [e-mail] · [phone]

MIT license.