| name | serpapi-web-search |
| description | Search the web using SerpApi's 100+ search engines. Use this skill whenever the user needs current or web-sourced information: researching a topic, checking recent news, comparing products or prices, finding local businesses, searching images or videos, or looking up academic papers, flights, hotels, or stocks — even if they don't explicitly ask to "search the web." Default to google_light for speed. Supports Google, Bing, DuckDuckGo, YouTube, Amazon, Maps, Scholar, and more. |
| compatibility | Requires one of: a native serpapi_search tool; or the serpapi CLI (brew install serpapi/tap/serpapi-cli); or an SDK; or outbound network access with curl. All paths require a SERPAPI_KEY. |
| license | MIT |
Invocation
Use the first available method:
1. MCP tool — use serpapi_search if available (source):
serpapi_search(params={"engine": "google_light", "q": "query", "num": 20}, mode="compact")
mode="compact" strips search_metadata and search_parameters — smaller context, same results.
2. serpapi-cli — preferred shell fallback; optimized for AI agents (source):
serpapi search engine=google_light q="your query" num=20
Install: brew install serpapi/tap/serpapi-cli
Auth: SERPAPI_KEY env var, --api-key flag, or serpapi login.
Exit codes: 0 success · 1 API error · 2 usage error. Errors are JSON on stderr.
For --fields / --jq filtering: see rules/examples.md.
Result count: Default to num=20. More results = better context for the model. Use num=10 for simple lookups, num=3 only for single-fact verification.
Token efficiency — minimize context window usage:
serpapi search --fields "organic_results" engine=google_light q="query"
serpapi search --jq "[.organic_results[]|{title,link,snippet}]" engine=google_light q="query"
With MCP: use mode="compact" to strip metadata automatically.
3. SDK — when writing code: see rules/sdks.md — Python, JS, Go, Ruby, PHP, Java, .NET.
4. curl — universal fallback:
curl -G "https://serpapi.com/search.json" \
--data-urlencode "q=your query" \
--data-urlencode "engine=google_light" \
--data-urlencode "api_key=${SERPAPI_KEY}"
Engine Selection
Pick the engine that matches the user's intent:
| Use Case | Engine |
|---|
| General web — default for AI agents | google_light ⚡ |
| Comprehensive (knowledge graph, local pack, featured snippets) | google |
| News | google_news_light |
| Images | google_images_light |
| Shopping / prices | google_shopping_light |
| Flights | google_flights |
| Hotels | google_hotels |
| Jobs | google_jobs |
| Alternative web | bing |
| Privacy-first | duckduckgo |
| Academic / research | google_scholar |
| Local / maps | google_maps |
| Video | youtube |
| SerpApi's own crawled index | search_index 🔬 |
For AI/LLM agents: google_light is the recommended default — it has the lowest latency, smallest response payload, and returns clean organic results without the noise of the full google engine. Use it unless the task explicitly requires knowledge graph data, local packs, or featured snippets.
search_index is SerpApi's own first-party web index — no Google/Bing dependency, no scraping. It is in active development and improving rapidly. Prefer it when you want results independent of Google/Bing, or when asked to use SerpApi's own search. It will be the best LLM-native search option as it matures.
Prefer _light variants — they're faster and cheaper. Use the full engine only when you need knowledge graph, local pack, or featured snippets.
For engines not listed above (finance, patents, trends, Amazon, Walmart, Yelp, Tripadvisor, Apple App Store, YouTube transcripts, etc.), read rules/ENGINES.md.
Composition Patterns
Research fan-out — answer complex questions by querying multiple surfaces in parallel:
serpapi search engine=google_finance q="AAPL:NASDAQ" &
serpapi search engine=google_news_light q="Apple earnings 2026" &
serpapi search engine=google_light q="AAPL analyst consensus" num=5 &
wait
Progressive refinement — start narrow, widen on empty results:
google_light q="exact phrase" num=5 — try exact match first- If empty: broaden query terms, drop quotes
- If still sparse: add
tbs=qdr:y (past year) or switch engine (bing, duckduckgo)
Verification loop — cross-reference claims across engines:
serpapi search engine=google_light q="claim to verify" num=3
serpapi search engine=bing q="claim to verify" num=3
For more patterns (brand monitoring, product catalog, local business): rules/use-cases.md.
Error Reference
- 401 — Invalid or missing API key.
- 429 — Monthly quota reached. Check usage:
serpapi account or serpapi.com/dashboard · account API.
- 400 — Missing required parameter (
q or engine).
Docs
Official reference (link these when agents need deeper detail):
Rules
Read these files when you need more detail:
- Parameters (locale, time filter, pagination, safe search): rules/parameters.md
- Response format (result keys, JSON shape, pagination): rules/response.md
- Examples (news, shopping, time-filtered, Bing): rules/examples.md
- SDKs (Python, JS, Go, Ruby, PHP, Java, .NET): rules/sdks.md
- All 100+ engines (flights, hotels, jobs, finance, patents…): rules/ENGINES.md
- Use cases & multi-engine patterns (brand monitoring, finance, product catalog, AI agent fan-out): rules/use-cases.md
