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SearchAPI Mcp

SearchAPI Mcp

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github.com/RmMargtSearch74Updated 49w ago

About SearchAPI Mcp

SearchAPI Mcp is an MCP server for AI assistants that exposes SearchAPI.io search across Google web, Maps, events, and travel services. It includes caching, retries, circuit breaking, and health metrics.

Key features

  1. 01Google web, video, and AI search
  2. 02Maps, places, and review search
  3. 03Events and travel searches
  4. 04Caching, retries, and circuit breaker
  5. 05Health checks and request metrics

Use cases

  1. 01Find web results and cited AI overviews
  2. 02Locate businesses and inspect reviews
  3. 03Search flights, hotels, and destinations

MCP support

Exposes SearchAPI.io search capabilities as tools to MCP clients, including web, Maps, events, and travel searches.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

SearchAPI MCP Server

License: MIT Python 3.10+ FastMCP

A production-ready Model Context Protocol (MCP) server providing comprehensive search capabilities through SearchAPI.io. Enable AI assistants to search Google, Maps, Flights, Hotels, and more with built-in caching, retry logic, and circuit breakers.

一个基于 Model Context Protocol (MCP) 的生产级搜索服务器,通过 SearchAPI.io 提供全面的搜索功能。使 AI 助手能够搜索 Google、地图、航班、酒店等,内置缓存、重试逻辑和熔断器。

Features • Quick Start • Installation • Configuration • Available Tools


Features

🔍 Search Engines
  • Google Search - Web results, knowledge graph, answer boxes, related questions
  • Google Videos - Video search with filtering by duration, source, and upload time
  • Google AI Mode - AI-generated overviews with cited sources and structured content
  • Google Maps - Places, businesses, reviews, and location details
  • Google Maps Place - Detailed information for specific locations (hours, photos, amenities)
  • Google Events - Find concerts, conferences, festivals, and local activities
  • Google Flights - Flight search with comprehensive filtering and price calendars
  • Google Flights Location Search - Airport code lookup and autocomplete
  • Google Travel Explore - Discover destinations and travel inspiration
  • Google Hotels - Accommodation search with amenities, ratings, and price filters
🏗️ Production-Ready Architecture
  • Connection Pooling - Efficient HTTP connection management with httpx
  • Response Caching - Configurable TTL-based caching with LRU eviction
  • Retry Logic - Exponential backoff for transient failures
  • Circuit Breaker - Fail-safe pattern preventing cascading failures
  • Metrics Collection - Request counts, latencies, cache hit rates, error tracking
  • Health Checks - Monitor API connectivity and service status
⚙️ Configuration & Monitoring
  • Pydantic Validation - Type-safe configuration with environment variable support
  • Structured Logging - Configurable log levels with detailed request tracing
  • Resource Management - Automatic cleanup and graceful shutdown
  • Environment Variables - Flexible configuration for different deployments

Quick Start

Prerequisites
Installation with UV (Recommended)

The fastest way to get started is using uvx:

# Set your API key
export SEARCHAPI_API_KEY="your_api_key_here"

# Run directly with uvx (no installation needed)
uvx --from git+https://github.com/RmMargt/searchAPI-mcp.git mcp-server-searchapi

Installation

Method 1: UV (Recommended)

UV is the fastest and most convenient method:

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository
git clone https://github.com/RmMargt/searchAPI-mcp.git
cd searchAPI-mcp

# Install dependencies
uv pip install -r requirements.txt
Method 2: pip
# Clone the repository
git clone https://github.com/RmMargt/searchAPI-mcp.git
cd searchAPI-mcp

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
Method 3: From Source
git clone https://github.com/RmMargt/searchAPI-mcp.git
cd searchAPI-mcp

# Using uv
uv pip install httpx fastmcp python-dotenv pydantic pydantic-settings

# Or using pip
pip install httpx fastmcp python-dotenv pydantic pydantic-settings

Configuration

Environment Variables

Create a .env file in the project root:

# Required
SEARCHAPI_API_KEY=your_api_key_here

# Optional - API Configuration
SEARCHAPI_API_URL=https://www.searchapi.io/api/v1/search
TIMEOUT=30.0
MAX_RETRIES=3
RETRY_BACKOFF=1.0

# Optional - Cache Configuration
ENABLE_CACHE=true
CACHE_TTL=3600
CACHE_MAX_SIZE=1000

# Optional - Connection Pool
POOL_CONNECTIONS=10
POOL_MAXSIZE=10

# Optional - Monitoring
ENABLE_METRICS=true
LOG_LEVEL=INFO
MCP Client Configuration
Claude Desktop

Add to your Claude Desktop configuration file:

Location:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
  • Linux: ~/.config/Claude/claude_desktop_config.json

Using UV (Recommended):

{
  "mcpServers": {
    "searchapi": {
      "command": "uvx",
      "args": [
        "--directory",
        "/absolute/path/to/searchAPI-mcp",
        "python",
        "mcp_server_refactored.py"
      ],
      "env": {
        "SEARCHAPI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Using Python directly:

{
  "mcpServers": {
    "searchapi": {
      "command": "python",
      "args": [
        "/absolute/path/to/searchAPI-mcp/mcp_server_refactored.py"
      ],
      "env": {
        "SEARCHAPI_API_KEY": "your_api_key_here"
      }
    }
  }
}

Using virtual environment:

{
  "mcpServers": {
    "searchapi": {
      "command": "/absolute/path/to/searchAPI-mcp/venv/bin/python",
      "args": [
        "/absolute/path/to/searchAPI-mcp/mcp_server_refactored.py"
      ],
      "env": {
        "SEARCHAPI_API_KEY": "your_api_key_here"
      }
    }
  }
}
VS Code (Continue, Cline)

Add to .vscode/mcp.json in your workspace or use the "MCP: Open User Configuration" command:

{
  "servers": {
    "searchapi": {
      "command": "python",
      "args": [
        "/absolute/path/to/searchAPI-mcp/mcp_server_refactored.py"
      ],
      "env": {
        "SEARCHAPI_API_KEY": "your_api_key_here"
      }
    }
  }
}

With UV:

{
  "servers": {
    "searchapi": {
      "command": "uvx",
      "args": [
        "--directory",
        "/absolute/path/to/searchAPI-mcp",
        "python",
        "mcp_server_refactored.py"
      ],
      "env": {
        "SEARCHAPI_API_KEY": "your_api_key_here"
      }
    }
  }
}
Zed Editor

Add to ~/.config/zed/settings.json:

{
  "context_servers": {
    "searchapi": {
      "command": {
        "path": "python",
        "args": [
          "/absolute/path/to/searchAPI-mcp/mcp_server_refactored.py"
        ],
        "env": {
          "SEARCHAPI_API_KEY": "your_api_key_here"
        }
      }
    }
  }
}
Cline (VS Code Extension)

In Cline settings, add to MCP Servers:

{
  "mcpServers": {
    "searchapi": {
      "command": "python",
      "args": [
        "/absolute/path/to/searchAPI-mcp/mcp_server_refactored.py"
      ],
      "env": {
        "SEARCHAPI_API_KEY": "your_api_key_here"
      }
    }
  }
}
Generic MCP Client

For any MCP-compatible client:

# Using stdio transport (default)
python /path/to/searchAPI-mcp/mcp_server_refactored.py

# With environment variable
SEARCHAPI_API_KEY=your_key python mcp_server_refactored.py

Available Tools

Health & Monitoring
health_check

Check the health and performance of the SearchAPI service.

Returns:

  • API connectivity status
  • Response latency
  • Circuit breaker state
  • Cache statistics
  • Request metrics

Example:

{
  "api_status": {
    "status": "healthy",
    "latency_ms": 145.23,
    "circuit_breaker": "closed"
  },
  "cache_stats": {
    "size": 42,
    "max_size": 1000,
    "ttl": 3600
  },
  "metrics": {
    "request_count": 156,
    "error_count": 2,
    "cache_hit_rate": 0.67
  }
}

Time & Date Utilities
get_current_time

Get current time and travel date suggestions. Essential for flight and hotel bookings.

Parameters:

  • format - Date format: "iso", "slash", "chinese", "timestamp", "full"
  • days_offset - Days from today (can be negative)
  • return_future_dates - Return array of future dates
  • future_days - Number of future dates (if return_future_dates=true)

Example:

# Get today's date in ISO format
get_current_time(format="iso")
# Returns: {"date": "2025-11-16", "now": {...}, "travel_dates": {...}}

# Get date 7 days from now with future dates array
get_current_time(days_offset=7, return_future_dates=True, future_days=30)

Google Search
search_google

Search Google for web results, knowledge graphs, and answer boxes.

Parameters:

  • q (required) - Search query
  • location - Location name (e.g., "New York, NY")
  • gl - Country code (default: "us")
  • hl - Language code (default: "en")
  • time_period - Time filter: "last_hour", "last_day", "last_week", "last_month", "last_year"
  • num - Results per page (default: "10")
  • safe - Safe search: "off", "active"

Example:

search_google(
    q="Python programming tutorials",
    location="San Francisco, CA",
    time_period="last_month",
    num="20"
)
search_google_videos

Search Google Videos for video content.

Parameters: Similar to search_google with video-specific filters

  • q (required) - Search query
  • time_period - Filter by upload time
  • device - "desktop" or "mobile"

Example:

search_google_videos(
    q="machine learning tutorial",
    time_period="last_week",
    num="10"
)
search_google_ai_mode

Search with AI-generated overviews and cited sources.

Parameters:

  • q - Search query (required unless url provided)
  • url - Image URL to search
  • location - Location for localized results

Returns:

  • AI-generated overview with citations
  • Structured content blocks (paragraphs, lists, tables, code)
  • Reference links
  • Web results

Example:

search_google_ai_mode(
    q="How does machine learning work?",
    location="United States"
)

Google Maps
search_google_maps

Search for places, businesses, and services.

Parameters:

  • query (required) - Search query
  • location_ll - Lat/lng coordinates (format: "@lat,lng,zoom")

Example:

search_google_maps(
    query="coffee shops near Central Park",
    location_ll="@40.7829,-73.9654,15z"
)
search_google_maps_place

Get detailed information for a specific place.

Parameters:

  • place_id (required if no data_id) - Google Maps place ID
  • data_id - Alternative place identifier
  • google_domain - Google domain (default: "google.com")
  • hl - Language code (default: "en")

Example:

search_google_maps_place(
    place_id="ChIJN1t_tDeuEmsRUsoyG83frY4"
)
search_google_maps_reviews

Get reviews for a specific place.

Parameters:

  • place_id (required if no data_id) - Google Maps place ID
  • data_id - Alternative place identifier
  • sort_by - "most_relevant", "newest", "highest_rating", "lowest_rating"
  • rating - Filter by rating: "1"-"5"

Example:

search_google_maps_reviews(
    place_id="ChIJN1t_tDeuEmsRUsoyG83frY4",
    sort_by="newest",
    rating="5"
)

Google Events
search_google_events

Search for events, concerts, conferences, and activities.

Parameters:

  • q (required) - Search query (e.g., "concerts in NYC", "tech conferences")
  • location - Location name for localized results
  • chips - Date filter ("today", "tomorrow", "week", "weekend", "month") or event type
  • gl - Country code (default: "us")
  • hl - Language code (default: "en")
  • page - Page number (default: "1")

Example:

search_google_events(
    q="music festivals in Austin",
    chips="weekend",
    location="Austin, TX"
)

Google Flights
search_google_flights

Search for flights with comprehensive filtering.

Parameters:

  • departure_id (required) - Airport code (e.g., "JFK")
  • arrival_id (required) - Airport code (e.g., "LAX")
  • outbound_date (required) - Departure date (YYYY-MM-DD)
  • flight_type - "one_way", "round_trip", "multi_city"
  • return_date - Return date (required for round_trip)
  • travel_class - "economy", "premium_economy", "business", "first"
  • stops - "0" (nonstop), "1", "2"
  • adults - Number of adults
  • currency - Currency code (e.g., "USD")

Example:

search_google_flights(
    departure_id="JFK",
    arrival_id="LAX",
    outbound_date="2025-12-15",
    return_date="2025-12-22",
    flight_type="round_trip",
    travel_class="economy",
    stops="0",
    adults="2"
)
search_google_flights_calendar

Get price calendar for flexible date planning.

Parameters:

  • flight_type (required) - "one_way" or "round_trip"
  • departure_id (required) - Airport code
  • arrival_id (required) - Airport code
  • outbound_date (required) - Reference date
  • return_date - Required for round_trip

Example:

search_google_flights_calendar(
    flight_type="round_trip",
    departure_id="SFO",
    arrival_id="NYC",
    outbound_date="2025-12-01",
    return_date="2025-12-08"
)
search_google_flights_location_search

Search for airport codes and locations.

Parameters:

  • q (required) - Search query (airport name, city, or code)
  • gl - Country code (default: "us")
  • hl - Language code (default: "en")

Example:

search_google_flights_location_search(
    q="Tokyo"
)
search_google_travel_explore

Explore travel destinations and find inspiration.

Parameters:

  • departure_id (required) - Departure airport code or location
  • arrival_id - Destination (defaults to anywhere)
  • time_period - Travel period (e.g., "two_week_trip_in_december")
  • interests - Filter by interests: "popular", "outdoors", "beaches", "museums", "history", "skiing"
  • travel_class - "economy", "premium_economy", "business", "first_class"
  • adults - Number of adults (default: "1")
  • currency - Currency code (default: "USD")

Example:

search_google_travel_explore(
    departure_id="JFK",
    interests="beaches",
    time_period="two_week_trip_in_december"
)

Google Hotels
search_google_hotels

Search for hotels and accommodation.

Parameters:

  • q (required) - Location query
  • check_in_date (required) - Check-in date (YYYY-MM-DD)
  • check_out_date (required) - Check-out date (YYYY-MM-DD)
  • adults - Number of adults (default: "2")
  • rating - Minimum rating: "3", "4", "5"
  • hotel_class - Star rating: "2"-"5"
  • price_min / price_max - Price range
  • amenities - Filter by amenities (e.g., "pool,wifi,parking")
  • free_cancellation - "true" or "false"

Example:

search_google_hotels(
    q="hotels in Paris",
    check_in_date="2025-12-20",
    check_out_date="2025-12-25",
    adults="2",
    rating="4",
    amenities="wifi,pool",
    price_max="300",
    free_cancellation="true"
)
search_google_hotels_property

Get detailed information for a specific hotel.

Parameters:

  • property_token (required) - Property ID from search results
  • check_in_date (required) - Check-in date
  • check_out_date (required) - Check-out date
  • adults - Number of adults

Example:

search_google_hotels_property(
    property_token="ChIJd8BlQ2BZwokRAFUEcm_qrcA",
    check_in_date="2025-12-20",
    check_out_date="2025-12-25",
    adults="2"
)

Usage Examples

Example 1: Discover and Plan a Trip
# 1. Explore destinations from New York
destinations = search_google_travel_explore(
    departure_id="JFK",
    interests="beaches",
    time_period="two_week_trip_in_december"
)

# 2. Get current date and travel dates
dates = get_current_time(return_future_dates=True, future_days=30)
check_in = dates["travel_dates"]["next_week"]
check_out = dates["travel_dates"]["next_month"]

# 3. Search for flights
flights = search_google_flights(
    departure_id="JFK",
    arrival_id="CDG",
    outbound_date=check_in,
    return_date=check_out,
    flight_type="round_trip",
    travel_class="economy",
    adults="2"
)

# 4. Search for hotels
hotels = search_google_hotels(
    q="hotels in Paris",
    check_in_date=check_in,
    check_out_date=check_out,
    adults="2",
    rating="4",
    amenities="wifi,breakfast"
)

# 5. Find nearby restaurants
restaurants = search_google_maps(
    query="restaurants near Eiffel Tower"
)

# 6. Get detailed place info
place_details = search_google_maps_place(
    place_id=restaurants["local_results"][0]["place_id"]
)

# 7. Find local events
events = search_google_events(
    q="concerts in Paris",
    chips="weekend"
)
Example 2: Research with AI Mode
# Get AI-generated overview with sources
result = search_google_ai_mode(
    q="What are the health benefits of Mediterranean diet?",
    location="United States"
)

# Result includes:
# - result["markdown"] - AI overview in markdown format
# - result["text_blocks"] - Structured content blocks
# - result["reference_links"] - Cited sources
# - result["web_results"] - Traditional search results
Example 3: Monitor Service Health
# Check API health and metrics
health = health_check()

print(f"Status: {health['api_status']['status']}")
print(f"Latency: {health['api_status']['latency_ms']}ms")
print(f"Cache hit rate: {health['metrics']['cache_hit_rate']:.2%}")
print(f"Total requests: {health['metrics']['request_count']}")

Development

Running Tests
# Run all tests
python -m pytest

# Run specific test file
python test_refactored.py
Code Structure
searchAPI-mcp/
├── mcp_server_refactored.py  # Main MCP server with FastMCP
├── config.py                  # Configuration with Pydantic validation
├── client.py                  # HTTP client with pooling, retry, caching
├── requirements.txt           # Python dependencies
├── .env.example              # Example environment variables
└── tests/                    # Test files
Key Components
  • mcp_server_refactored.py - MCP server implementation using FastMCP

    • Tool definitions with comprehensive docstrings
    • Health checks and monitoring endpoints
    • Clean shutdown and resource management
  • config.py - Configuration management

    • Pydantic models for type-safe configuration
    • Environment variable validation
    • Sensible defaults with override options
  • client.py - Production-ready HTTP client

    • Connection pooling with httpx
    • Exponential backoff retry logic
    • TTL-based response caching
    • Circuit breaker pattern
    • Metrics collection
Debugging

Use the MCP Inspector to test tools:

# Install inspector
npm install -g @modelcontextprotocol/inspector

# Run inspector
npx @modelcontextprotocol/inspector python mcp_server_refactored.py

Set environment variable for verbose logging:

LOG_LEVEL=DEBUG python mcp_server_refactored.py

Troubleshooting

Common Issues

Issue: "Invalid API key" error

  • Solution: Ensure SEARCHAPI_API_KEY is set correctly in environment or .env file
  • Get an API key at https://www.searchapi.io/

Issue: "Circuit breaker is OPEN" error

  • Cause: Too many consecutive API failures
  • Solution: Check your internet connection and API key. Wait 60 seconds for circuit to reset, or restart the server

Issue: Connection timeouts

  • Solution: Increase timeout in .env: TIMEOUT=60.0
  • Check network connectivity to SearchAPI.io

Issue: High latency

  • Solution: Enable caching if disabled: ENABLE_CACHE=true
  • Increase cache size: CACHE_MAX_SIZE=5000
  • Check health_check tool for metrics

Issue: Windows encoding errors

  • Solution: Set environment variable in your MCP client config:
    "env": {
      "PYTHONIOENCODING": "utf-8",
      "SEARCHAPI_API_KEY": "your_key"
    }
    
Getting Help

Performance Tuning

Cache Configuration

For higher cache hit rates:

CACHE_TTL=7200        # 2 hours
CACHE_MAX_SIZE=5000   # Store more results

For memory-constrained environments:

CACHE_TTL=1800        # 30 minutes
CACHE_MAX_SIZE=100    # Smaller cache
Connection Pool

For high-traffic scenarios:

POOL_CONNECTIONS=50
POOL_MAXSIZE=50

For low-traffic scenarios:

POOL_CONNECTIONS=5
POOL_MAXSIZE=5

Security Considerations

⚠️ Important Security Notes:

  1. API Key Protection

    • Never commit .env file to version control
    • Use environment variables in production
    • Rotate API keys regularly
  2. Rate Limiting

    • SearchAPI.io has rate limits based on your plan
    • Built-in retry logic respects rate limits
    • Monitor usage with health_check tool
  3. Data Privacy

    • Search queries are sent to SearchAPI.io
    • Responses are cached locally (configurable)
    • Review SearchAPI.io privacy policy

License

This project is licensed under the MIT License - see the LICENSE file for details.


Acknowledgments


Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

Changelog

v1.1.0 (Current)
  • ✅ New: Google Maps Place API - Detailed place information
  • ✅ New: Google Events API - Event and activity search
  • ✅ New: Google Travel Explore API - Destination discovery
  • ✅ New: Google Flights Location Search API - Airport lookup
  • ✅ Enhanced tourism and travel research capabilities
  • ✅ Comprehensive travel planning workflow support
v1.0.0
  • ✅ Production-ready architecture with FastMCP
  • ✅ Connection pooling and retry logic
  • ✅ Response caching with TTL
  • ✅ Circuit breaker pattern
  • ✅ Comprehensive health checks and metrics
  • ✅ Google Search, Videos, AI Mode
  • ✅ Google Maps and Reviews
  • ✅ Google Flights and Calendar
  • ✅ Google Hotels and Property details
  • ✅ Time utilities for travel planning

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Made with ❤️ for the MCP community

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.