# Start from your application's goal

You rarely need every search capability. Give the agent the smallest useful set, name specialized configurations clearly, and keep policy choices such as locale, currency, and safe search in `default_params`. Use `result_limit` for the number of results returned by the tool.

The examples below use automatic SDK detection. They work as written when one supported agent SDK is installed. If multiple SDKs share the environment, add the same explicit `provider=` to each constructor.


# General web research

Use [`web_search`](web_search.md) for public websites and background sources:

``` python
from serpapi_search_tools import web_search

tools = [
    web_search(
        allowed_engines=["google_light", "bing"],
        default_engine="google_light",
        result_limit=5,
    )
]
```

Good first prompt: "Research three independent sources about Python package signing and summarize where they agree."


# Current reporting

Combine [`news_search`](news_search.md) with web search when the agent needs both recent coverage and durable background information:

``` python
from serpapi_search_tools import news_search, web_search

tools = [web_search(), news_search(default_params={"hl": "en", "gl": "us"})]
```

Good first prompt: "Find today's reporting about battery recycling, then use background sources to explain the context."


# Local discovery

Use [`maps_search`](maps_search.md) for businesses and places. Location, zoom, and nearby intent are typed model inputs:

``` python
from serpapi_search_tools import maps_search

tools = [maps_search(default_params={"hl": "en", "gl": "us"})]
```

Good first prompt: "Find three independent coffee roasters near Portland, Oregon, and compare ratings, hours, and location."


# Image discovery

Use [`images_search`](images_search.md) for image URLs and visual metadata. Safe search can be an application policy rather than a model choice:

``` python
from serpapi_search_tools import images_search

tools = [images_search(default_params={"safe": "active"})]
```

Good first prompt: "Find visual references for compact balcony gardens and summarize recurring layout ideas."


# Marketplace comparison

Use [`shopping_search`](shopping_search.md) for product listings. Separate instances are easiest when marketplaces need different defaults:

``` python
from serpapi_search_tools import shopping_search

tools = [
    shopping_search(
        allowed_engines=["google_shopping"],
        default_params={"gl": "us", "hl": "en"},
        result_limit=5,
        name="google_products",
    ),
    shopping_search(
        allowed_engines=["amazon"],
        result_limit=5,
        name="amazon_products",
    ),
]
```

Good first prompt: "Compare noise-cancelling headphones from both marketplaces and call out price or availability differences."


# Video discovery

Use [`videos_search`](videos_search.md) for YouTube videos, channels, and playlists:

``` python
from serpapi_search_tools import videos_search

tools = [videos_search(default_params={"hl": "en", "gl": "us"})]
```

Good first prompt: "Find a beginner pour-over coffee tutorial and explain why it is a suitable first lesson."


# Hotel availability

Use [`hotels_search`](hotels_search.md) when the destination and stay dates are known:

``` python
from serpapi_search_tools import hotels_search

tools = [hotels_search(default_params={"currency": "USD", "gl": "us"})]
```

The prompt should include future dates and occupancy, for example: "Find hotels in Kyoto from 2030-08-01 through 2030-08-04 for two adults and one child age 8."


# Known-route flights

Use [`flights_search`](flights_search.md) when origin and destination are known:

``` python
from serpapi_search_tools import flights_search

tools = [flights_search(default_params={"currency": "USD", "hl": "en"})]
```

Good first prompt: "Compare round-trip business-class flights from LAX to AUS, departing 2030-08-01 and returning 2030-08-04."


# Destination ideas

Use [`travel_explore_search`](travel_explore_search.md) when the departure is known but the destination is open:

``` python
from serpapi_search_tools import travel_explore_search

tools = [travel_explore_search(default_params={"currency": "USD", "gl": "us"})]
```

Good first prompt: "Starting from JFK, suggest three destinations for a four-day trip and compare indicative fares."


# Monitor a product launch

This combination separates background pages, current reporting, and live product listings:

``` python
from serpapi_search_tools import news_search, shopping_search, web_search

tools = [web_search(), news_search(), shopping_search()]
```

Ask the agent to use web search for official specifications, news search for recent announcements and reviews, and shopping search for current listings. This is more reliable than expecting one result type to answer every question.


# Research a move or neighborhood

Use the web for city information, maps for specific places, and images for a visual sense of the area:

``` python
from serpapi_search_tools import images_search, maps_search, web_search

tools = [web_search(), maps_search(), images_search()]
```

Good first prompt: "Research living near the Seattle Convention Center. Find grocery stores and parks, then gather visual references of the surrounding neighborhood."


# Compare a purchase visually

Combine listings, product imagery, and video demonstrations:

``` python
from serpapi_search_tools import images_search, shopping_search, videos_search

tools = [shopping_search(), images_search(), videos_search()]
```

Good first prompt: "Compare three compact espresso machines by price, physical design, and the availability of useful setup tutorials."


# Plan a trip from an open brief

Give each stage of the planning process its own typed tool:

``` python
from serpapi_search_tools import (
    flights_search,
    hotels_search,
    maps_search,
    travel_explore_search,
)

tools = [
    travel_explore_search(),
    flights_search(),
    hotels_search(),
    maps_search(),
]
```

The agent can explore destinations first, search an exact route after choosing one, find properties for explicit dates, and finally find nearby places. A good prompt includes origin, date flexibility, passenger count, budget, and travel preferences.


# Keep agent payloads manageable

Every tool returns a compact response by default. Set `result_limit` on the tool constructor to control how many items are kept in each result list. Use a custom `client=` for application-specific logging or caching. Choose [SearchResultMode.FULL](../reference/SearchResultMode.md#serpapi_search_tools.SearchResultMode.FULL) when application code needs supporting response sections or additional result fields.

Continue with [Runnable examples](examples.md), [Common configuration](configuration.md), or [Choose a search tool](search_tools.md).