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SerpApi MCP Server

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Retrieve parsed search engine results using the SerpApi.

🔥🔥🔥🔥✓ VerifiedFreeAdvanced setup
#automation#scraping#devops#git#search#maps#weather#finance

<img src="https://user-images.githubusercontent.com/307597/154772945-1b7dba5f-21cf-41d0-bb2e-65b6eff4aaaf.png" width="30" height="30"/> SerpApi MCP Server

A Model Context Protocol (MCP) server implementation that integrates with SerpApi for comprehensive search engine results and data extraction.

Python 3.13+ MIT License Install in VS Code Install in Cursor

Features

  • Multi-Engine Search: Google, Bing, Yahoo, DuckDuckGo, YouTube, eBay, and more
  • Engine Resources: Per-engine parameter schemas available via MCP resources (see Search Tool)
  • Real-time Weather Data: Location-based weather with forecasts via search queries
  • Stock Market Data: Company financials and market data through search integration
  • Dynamic Result Processing: Automatically detects and formats different result types
  • Flexible Response Modes: Complete or compact JSON responses
  • JSON Responses: Structured JSON output with complete or compact modes
  • Interactive UI (MCP Apps): Opt-in search_table and search_dashboard tools that render results as an interactive UI in supporting hosts

Authentication

Two methods are supported:

  • Path-based: /YOUR_API_KEY/mcp (recommended)
  • Header-based: Authorization: Bearer YOUR_API_KEY

Examples:

Copy & paste — that's it
# Path-based
curl "https://mcp.serpapi.com/your_key/mcp" -d '...'

# Header-based  
curl "https://mcp.serpapi.com/mcp" -H "Authorization: Bearer your_key" -d '...'

Search Tool

The MCP server has one main Search Tool that supports all SerpApi engines and result types. You can find all available parameters on the SerpApi API reference. Engine parameter schemas are also exposed as MCP resources: serpapi://engines (index) and serpapi://engines/<engine>.

The parameters you can provide are specific for each API engine. Some sample parameters are provided below:

  • params.q (required): Search query
  • params.engine: Search engine (default: "google_light")
  • params.location: Geographic filter
  • mode: Response mode - "complete" (default) or "compact"
  • ...see other parameters on the SerpApi API reference

Examples:

Copy & paste — that's it
{"name": "search", "arguments": {"params": {"q": "coffee shops", "location": "Austin, TX"}}}
{"name": "search", "arguments": {"params": {"q": "weather in London"}}}
{"name": "search", "arguments": {"params": {"q": "AAPL stock"}}}
{"name": "search", "arguments": {"params": {"q": "news"}, "mode": "compact"}}
{"name": "search", "arguments": {"params": {"q": "detailed search"}, "mode": "complete"}}

Supported Engines: Google, Bing, Yahoo, DuckDuckGo, YouTube, eBay, and more (see serpapi://engines).

Result Types: Answer boxes, organic results, news, images, shopping - automatically detected and formatted.

Interactive UI (MCP Apps)

The default search tool returns JSON and is unchanged. For hosts that support the MCP Apps extension (SEP-1865), two opt-in tools render results as an interactive UI directly in the conversation, so the bulk SERP JSON never enters the model's context window:

  • search_table: organic results as a sortable, searchable table.
  • search_dashboard: summary metrics, a source-breakdown chart, and a results table with a click-to-expand detail panel.

Both accept the same params as search. Hosts that don't support MCP Apps simply ignore these tools.

Preview them locally without an MCP host:

Copy & paste — that's it
uv run fastmcp dev apps src/server.py

Development

Copy & paste — that's it
# Local development
uv sync && uv run src/server.py

# Docker
docker build -t serpapi-mcp . && docker run -p 8000:8000 serpapi-mcp

# Regenerate engine resources (Playground scrape)
python build-engines.py

# Testing with MCP Inspector
npx @modelcontextprotocol/inspector
# Configure: URL mcp.serpapi.com/YOUR_KEY/mcp, Transport "Streamable HTTP transport"

Contributing

  1. Fork the repository
  2. Create your feature branch: git checkout -b feature/amazing-feature
  3. Install dependencies: uv install
  4. Make your changes
  5. Commit changes: git commit -m 'Add amazing feature'
  6. Push to branch: git push origin feature/amazing-feature
  7. Open a Pull Request

License

MIT License - see LICENSE file for details.