Agno Agent to search for AirBnB Listings

airbnb_mcp-gemini.py

# airbnb_mcp.py
from textwrap import dedent

from agno.agent import Agent
from agno.models.google import Gemini
from agno.tools.mcp import MCPTools
from agno.tools.reasoning import ReasoningTools
from agno.os import AgentOS


# Create the Airbnb MCP tools (uses npx under the hood)
# Either of these forms typically works depending on your agno build:
# mcp_tools = MCPTools("npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt")
mcp_tools = MCPTools(
    command="npx -y @openbnb/mcp-server-airbnb --ignore-robots-txt",
    # env={"AIRBNB_COOKIES": "..."}  # optional: if you need cookies/headers
)

agent = Agent(
    model=Gemini(id="gemini-2.5-flash"),
    tools=[ReasoningTools(add_instructions=True), mcp_tools],
    instructions=dedent("""\
    ## Search & Ranking Rules for Airbnb
    - Use the Airbnb MCP tools to search and fetch listing details (title, price/night, rating, reviews, beds/bedrooms, location, link).
    - Normalize currency to INR when possible and show both INR and local currency if present.
    - Return a Top 10 table with: Title, Price/Night, Rating, Reviews, Beds/Bedrooms, Location, Link, Notes.
    - Briefly justify the ranking (value, location, reviews, host reliability).

    ## Think Tool Usage
    - Before each tool call, outline the plan and key filters.
    - After tool results, validate fields (e.g., parse "4.92 (138)" into rating=4.92, reviews=138).
    - Summarize trade-offs (price vs location vs rating) before final answer.
    """),
    add_datetime_to_context=True,
    markdown=True,
)

# --- Proper lifecycle wiring for MCPTools (async context manager) ---
async def _mcp_start():
    # Start the MCP subprocess
    await mcp_tools.__aenter__()

async def _mcp_stop():
    # Cleanly stop the MCP subprocess
    await mcp_tools.__aexit__(None, None, None)

agent_os = AgentOS(agents=[agent])
app = agent_os.get_app()