Google-Search-MCP by 1999AZZAR - MCP Server

Google Search MCP Server

A comprehensive Model Context Protocol (MCP) server that provides advanced Google Custom Search functionality, web content extraction, search analytics, and specialized research tools. This server transforms Google's search capabilities into powerful AI tools that can be integrated with any MCP-compatible AI client.

Features

Advanced Google Search: Perform web searches with extensive filtering options, file type restrictions, and geographic targeting
Content Extraction: Extract main content from web pages with automatic sentiment analysis
Search Analytics: Analyze search trends across multiple queries with comprehensive insights and keyword extraction
Multi-Site Search: Search across multiple websites simultaneously with detailed statistics
News Monitoring: Monitor news sources with topic filtering and date restrictions
Academic Research: Specialized tools for finding academic papers and research documents
Content Summarization: Intelligent summarization of multiple URLs with sentiment analysis and insights
Fact Checking: Automated fact verification with evidence-based results
Search Trends: Real-time search interest analysis and trend prediction
Cached Resources: 8 specialized resources providing cached search results, analytics, and research data
MCP Compatible: Seamlessly integrates with any MCP-compatible AI client (Claude, Cursor, etc.)
Robust Error Handling: Comprehensive error handling for API failures, rate limiting, and invalid parameters
Intelligent Caching: TTL-based caching system optimizing performance and API usage
TypeScript: Fully typed with Zod schema validation for all parameters

Prerequisites

Node.js 18+
Google Custom Search API key
Google Custom Search Engine ID

Installation

Clone this repository:

git clone https://github.com/1999AZZAR/mcp-server-google-search.git
cd mcp-server-google-search

Install dependencies:

npm install

Build the project:

npm run build

Verify installation:

# Test that the server starts correctly
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | GOOGLE_API_KEY=test GOOGLE_CSE_ID=test node dist/index.js

Configuration

Environment Variables

Create a .env file in the project root with the following variables:

GOOGLE_API_KEY=your_google_api_key_here
GOOGLE_CSE_ID=your_custom_search_engine_id_here

Getting Google API Credentials

Step 1: Google Cloud Console Setup

Go to the Google Cloud Console
Create a new project or select an existing one
Enable the "Custom Search API" in the API Library
Go to "Credentials" → "Create Credentials" → "API Key"
Copy your API key

Step 2: Custom Search Engine Setup

Go to Google Custom Search Engine
Click "Add" to create a new search engine
Enter the sites you want to search (or leave blank for entire web)
Give your search engine a name
Click "Create"
Go to "Setup" → "Basics" and copy your "Search engine ID"

Step 3: Configure Search Engine (Optional)

Search the entire web: Leave "Sites to search" empty
Search specific sites: Add domains like github.com, stackoverflow.com
Advanced settings: Configure language, region, and other preferences

Security Best Practices

Never commit your .env file to version control
Use environment variables in production
Consider using Google Cloud Secret Manager for production deployments
Restrict your API key to specific IP addresses if possible

Usage

As an MCP Server

For Cursor IDE

Add this server to your Cursor MCP configuration (~/.cursor/mcp.json):

{
  "mcpServers": {
    "google-search-mcp": {
      "command": "node",
      "args": ["/path/to/mcp-server-google-search/dist/index.js"],
      "env": {
        "GOOGLE_API_KEY": "your_api_key",
        "GOOGLE_CSE_ID": "your_cse_id"
      }
    }
  }
}

For Claude Desktop

Add this server to your Claude Desktop configuration (claude_desktop_config.json):

{
  "mcpServers": {
    "google-search-mcp": {
      "command": "node",
      "args": ["/path/to/mcp-server-google-search/dist/index.js"],
      "env": {
        "GOOGLE_API_KEY": "your_api_key",
        "GOOGLE_CSE_ID": "your_cse_id"
      }
    }
  }
}

For Other MCP Clients

The server follows the standard MCP protocol and should work with any MCP-compatible client. Refer to your client's documentation for configuration details.

Testing the Server

You can test the server directly using JSON-RPC commands:

# List all available tools
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | GOOGLE_API_KEY=your_key GOOGLE_CSE_ID=your_id node dist/index.js

# Test a search
echo '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"google_search","arguments":{"q":"test search","num":2}}}' | GOOGLE_API_KEY=your_key GOOGLE_CSE_ID=your_id node dist/index.js

Available Tools

This MCP server provides 10 powerful tools for comprehensive search, research, fact verification, trend analysis, and advanced research assistance:

1. Google Search (`google_search`)

Perform advanced web searches with extensive filtering options and geographic targeting.

Parameters:

q (required): Search query string
fileType (optional): File type filter - "pdf", "doc", "docx", "ppt", "pptx", "xls", "xlsx", "rtf"
siteSearch (optional): Search within a specific site (e.g., "example.com")
dateRestrict (optional): Date restriction - "d1", "w1", "m1", "y1", "d7", "w2", "m2", "y2", "m6", "y"
safe (optional): Safe search level - "active", "off"
exactTerms (optional): Terms that must appear exactly as specified
excludeTerms (optional): Terms to exclude from search results
sort (optional): Sort order - "date"
gl (optional): Country code for geolocation (e.g., "us", "uk")
hl (optional): Language code for interface (e.g., "en", "es")
num (optional): Number of results to return (1-10)
start (optional): Starting index for results (1-based)

Use Cases:

General web searches with advanced filtering
Finding specific file types (PDFs, documents)
Searching within specific websites
Time-restricted searches for recent content

Example:

{
  "name": "google_search",
  "arguments": {
    "q": "artificial intelligence",
    "num": 5,
    "fileType": "pdf",
    "dateRestrict": "m1"
  }
}

Response Format:

{
  "searchInfo": {
    "totalResults": "8420000",
    "searchTime": 0.626612,
    "formattedSearchTime": "0.63"
  },
  "items": [
    {
      "title": "Article Title",
      "link": "https://example.com/article",
      "snippet": "Article preview...",
      "displayLink": "example.com",
      "formattedUrl": "https://example.com/article"
    }
  ]
}

2. Extract Content (`extract_content`)

Extract main content from web pages and perform automatic sentiment analysis using advanced text processing.

Parameters:

url (required): URL of the web page to extract content from

Use Cases:

Summarizing articles and blog posts
Analyzing sentiment of news articles or reviews
Extracting clean text from web pages
Content analysis for research purposes

Example:

{
  "name": "extract_content",
  "arguments": {
    "url": "https://example.com/article"
  }
}

Response Format:

{
  "url": "https://example.com/article",
  "title": "Article Title",
  "content": "Extracted main content...",
  "wordCount": 1250,
  "sentiment": {
    "score": 0.8,
    "comparative": 0.15,
    "positive": 0.75,
    "negative": 0.25,
    "neutral": 0.0
  },
  "summary": "Brief summary of the content..."
}

3. Search Analytics (`search_analytics`)

Analyze search trends across multiple queries with comprehensive insights, keyword extraction, and performance metrics.

Parameters:

queries (required): Array of search queries to analyze (1-5 queries)
timeRange (optional): Time range for trend analysis - "week", "month", "year"
maxResults (optional): Maximum results per query (1-5)

Use Cases:

Market research and trend analysis
Keyword research for SEO
Competitive analysis
Content strategy planning
Brand monitoring

Example:

{
  "name": "search_analytics",
  "arguments": {
    "queries": ["artificial intelligence", "machine learning", "deep learning"],
    "timeRange": "month",
    "maxResults": 3
  }
}

Response Format:

{
  "queries": ["artificial intelligence", "machine learning", "deep learning"],
  "timeRange": "month",
  "results": [
    {
      "query": "artificial intelligence",
      "resultCount": 1600000000,
      "items": [...]
    }
  ],
  "summary": {
    "totalResults": 7060000000,
    "averageResults": 2353333333.33,
    "topPerformingQuery": "deep learning",
    "commonKeywords": ["learning", "artificial", "intelligence", "machine", "deep"]
  }
}

4. Multi-Site Search (`multi_site_search`)

Search across multiple specific websites simultaneously with detailed statistics and comprehensive results aggregation.

Parameters:

query (required): Search query
sites (required): Array of websites to search (1-5 sites)
maxResults (optional): Max results per site (1-5)
fileType (optional): File type to search for

Use Cases:

Cross-platform research (GitHub, Stack Overflow, Medium)
Competitive analysis across multiple sites
Finding resources on specific platforms
Aggregating information from trusted sources

Example:

{
  "name": "multi_site_search",
  "arguments": {
    "query": "react tutorial",
    "sites": ["github.com", "stackoverflow.com", "dev.to"],
    "maxResults": 3
  }
}

Response Format:

{
  "query": "react tutorial",
  "sites": ["github.com", "stackoverflow.com", "dev.to"],
  "results": [
    {
      "site": "github.com",
      "resultCount": 2,
      "totalAvailable": 19800,
      "items": [...]
    }
  ],
  "summary": {
    "totalResults": 6,
    "sitesSearched": 3,
    "successfulSearches": 3
  }
}

5. News Monitor (`news_monitor`)

Monitor news sources for specific topics with advanced filtering, source targeting, and date restrictions for real-time news intelligence.

Parameters:

topic (required): Topic to monitor
sources (optional): Array of news sources to monitor (e.g., ["bbc.com", "cnn.com", "reuters.com"])
language (optional): Language code (e.g., "en", "es")
country (optional): Country code (e.g., "us", "uk")
maxResults (optional): Maximum results to return (1-10)
dateRestrict (optional): Date restriction for news - "d1", "d7", "m1", "m6", "y1"

Use Cases:

Real-time news monitoring
Brand and reputation management
Crisis communication monitoring
Industry trend tracking
Competitive intelligence

Example:

{
  "name": "news_monitor",
  "arguments": {
    "topic": "artificial intelligence breakthrough",
    "sources": ["bbc.com", "cnn.com", "reuters.com"],
    "dateRestrict": "d7",
    "maxResults": 5
  }
}

Response Format:

{
  "topic": "artificial intelligence breakthrough",
  "sources": ["bbc.com", "cnn.com", "reuters.com"],
  "language": "en",
  "country": "us",
  "dateRestrict": "d7",
  "results": [
    {
      "source": "bbc.com",
      "articles": [...]
    }
  ],
  "summary": {
    "totalArticles": 15,
    "sourcesFound": 3,
    "dateRange": "d7"
  }
}

6. Academic Search (`academic_search`)

Search academic papers and research documents from specialized academic sources with PDF filtering and publication date restrictions.

Parameters:

query (required): Research query
fileType (optional): File type (PDF only) - "pdf"
dateRange (optional): Publication date range - "d1", "d7", "m1", "m6", "y1", "y2"
sites (optional): Academic sites to search (default: ["arxiv.org", "scholar.google.com", "researchgate.net"])
maxResults (optional): Maximum results to return (1-10)

Use Cases:

Academic research and literature reviews
Finding recent research papers
PhD and thesis research
Scientific literature analysis
Research trend monitoring

Example:

{
  "name": "academic_search",
  "arguments": {
    "query": "machine learning algorithms neural networks",
    "fileType": "pdf",
    "dateRange": "y1",
    "sites": ["arxiv.org", "scholar.google.com"],
    "maxResults": 5
  }
}

Response Format:

{
  "query": "machine learning algorithms neural networks",
  "fileType": "pdf",
  "dateRange": "y1",
  "sites": ["arxiv.org", "scholar.google.com"],
  "results": [
    {
      "site": "arxiv.org",
      "paperCount": 3,
      "totalAvailable": 18800,
      "papers": [
        {
          "title": "A Digital Machine Learning Algorithm Simulating Spiking Neural Networks",
          "link": "https://arxiv.org/pdf/2503.17111",
          "snippet": "During last several years, our research team worked on development of a spiking neural network...",
          "mime": "application/pdf",
          "fileFormat": "PDF/Adobe Acrobat"
        }
      ]
    }
  ],
  "summary": {
    "totalPapers": 3,
    "sitesSearched": 2,
    "successfulSearches": 2,
    "dateRange": "y1"
  }
}

7. Content Summarizer (`content_summarizer`)

Extract and summarize content from multiple URLs with intelligent summarization, sentiment analysis, and comprehensive insights.

Parameters:

urls (required): Array of URLs to summarize (1-10 URLs)
maxLength (optional): Maximum length of summary per URL in words (50-500, default: 200)
includeSentiment (optional): Include sentiment analysis for each URL (default: true)
focusAreas (optional): Specific areas to focus on in summaries (e.g., ["key points", "conclusions", "data"])
generateOverallSummary (optional): Generate an overall summary combining all URLs (default: true)

Use Cases:

Research summarization across multiple sources
Content analysis and comparison
News aggregation and analysis
Academic paper summarization
Competitive intelligence gathering
Content curation and insights

Example:

{
  "name": "content_summarizer",
  "arguments": {
    "urls": [
      "https://example.com/article1",
      "https://example.com/article2",
      "https://example.com/article3"
    ],
    "maxLength": 150,
    "includeSentiment": true,
    "focusAreas": ["key insights", "conclusions", "data"],
    "generateOverallSummary": true
  }
}

Response Format:

{
  "urls": ["https://example.com/article1", "https://example.com/article2"],
  "maxLength": 150,
  "includeSentiment": true,
  "focusAreas": ["key insights", "conclusions"],
  "generateOverallSummary": true,
  "summaries": [
    {
      "url": "https://example.com/article1",
      "title": "Article Title",
      "summary": "Key insights from the article...",
      "wordCount": 1250,
      "sentiment": {
        "score": 0.8,
        "comparative": 0.15,
        "positive": ["excellent", "innovative"],
        "negative": ["challenging"]
      },
      "extractionTime": "2024-01-15T10:30:00.000Z"
    }
  ],
  "overallSummary": "Combined insights from all articles...",
  "statistics": {
    "totalUrls": 2,
    "successfulExtractions": 2,
    "failedExtractions": 0,
    "averageWordCount": 1250,
    "sentimentDistribution": {
      "positive": 1,
      "negative": 0,
      "neutral": 1
    }
  }
}

8. Fact Checker (`fact_checker`)

Verify claims by searching multiple authoritative sources with credibility analysis and evidence extraction.

Parameters:

claim (required): The claim or statement to verify (minimum 10 characters)
sources (optional): Specific authoritative sources to check (e.g., ["wikipedia.org", "bbc.com", "reuters.com"])
confidenceThreshold (optional): Minimum confidence level for verification (0.0-1.0, default: 0.7)
timeframe (optional): Time range for search results - "d1", "d7", "m1", "m6", "y1", "y2" (default: "y1")
maxResults (optional): Maximum results per source (1-5, default: 3)
includeEvidence (optional): Include extracted evidence snippets (default: true)

Default Sources:

wikipedia.org, bbc.com, reuters.com, ap.org
factcheck.org, snopes.com, politifact.com
scholar.google.com, pubmed.ncbi.nlm.nih.gov, nature.com

Use Cases:

Fact verification and debunking misinformation
Research validation across multiple sources
News verification and credibility assessment
Academic claim verification
Public statement fact-checking
Scientific claim validation

Example:

{
  "name": "fact_checker",
  "arguments": {
    "claim": "The Earth is approximately 4.5 billion years old",
    "sources": ["wikipedia.org", "science.org", "nature.com"],
    "confidenceThreshold": 0.8,
    "timeframe": "y1",
    "maxResults": 2,
    "includeEvidence": true
  }
}

Response Format:

{
  "claim": "The Earth is approximately 4.5 billion years old",
  "sourcesToCheck": ["wikipedia.org", "science.org", "nature.com"],
  "confidenceThreshold": 0.8,
  "timeframe": "y1",
  "maxResults": 2,
  "includeEvidence": true,
  "verification": {
    "status": "verified",
    "confidence": 0.85,
    "evidenceCount": 4,
    "supportingSources": ["wikipedia.org", "science.org"],
    "disputingSources": [],
    "neutralSources": ["nature.com"]
  },
  "sources": [
    {
      "source": "wikipedia.org",
      "resultCount": 2,
      "totalAvailable": "3700",
      "results": [
        {
          "title": "Age of Earth - Wikipedia",
          "link": "https://en.wikipedia.org/wiki/Age_of_Earth",
          "snippet": "The age of Earth is estimated to be 4.54 ± 0.05 billion years...",
          "displayLink": "en.wikipedia.org",
          "relevanceScore": 0.8
        }
      ],
      "credibilityScore": 0.8
    }
  ],
  "evidence": [
    {
      "source": "wikipedia.org",
      "url": "https://en.wikipedia.org/wiki/Age_of_Earth",
      "title": "Age of Earth - Wikipedia",
      "evidence": "The age of Earth is estimated to be 4.54 ± 0.05 billion years. This age represents the final stages of Earth's accretion and planetary differentiation.",
      "relevanceScore": 0.8,
      "sentiment": {
        "score": 0,
        "comparative": 0
      }
    }
  ],
  "statistics": {
    "totalSourcesChecked": 3,
    "successfulSearches": 3,
    "failedSearches": 0,
    "totalResults": 6,
    "averageRelevanceScore": 0.75
  }
}

Verification Statuses:

verified: Claim is supported by credible sources with high confidence
disputed: Claim is contradicted by credible sources
unverified: Insufficient evidence or conflicting information
unknown: No relevant information found

9. Research Assistant (`research_assistant`)

10. Search Trends (`search_trends`)

Track and analyze search interest trends over time with predictive insights and emerging topic discovery. This tool provides comprehensive trend analysis by combining current search data with historical patterns to identify emerging topics and predict future interest levels.

Parameters:

topics (required): Array of topics to track trends for (1-5 topics)
timeframe (optional): Time period to analyze ('1M', '3M', '6M', '1Y', '2Y') - defaults to '6M'
region (optional): Geographic region for trend analysis (country code like 'US', 'GB', 'CA') - defaults to 'US'
category (optional): Category filter for more targeted trend analysis ('all', 'business', 'entertainment', 'health', 'politics', 'science', 'sports', 'technology') - defaults to 'all'
includePredictions (optional): Include trend prediction and forecasting - defaults to true
relatedTopics (optional): Discover and include related trending topics - defaults to true

Use Cases:

Market research and trend analysis
Content strategy and topic planning
Competitive analysis and market intelligence
Emerging technology tracking
Brand monitoring and reputation management
Seasonal trend analysis
Predictive content planning

Example:

{
  "name": "search_trends",
  "arguments": {
    "topics": ["artificial intelligence", "machine learning"],
    "timeframe": "6M",
    "region": "US",
    "category": "technology",
    "includePredictions": true,
    "relatedTopics": true
  }
}

Response Format:

{
  "topics": ["artificial intelligence", "machine learning"],
  "timeframe": "6M",
  "region": "US",
  "category": "technology",
  "includePredictions": true,
  "relatedTopics": true,
  "trends": [
    {
      "topic": "artificial intelligence",
      "currentInterest": 245000000,
      "recentActivity": 3,
      "trendDirection": "increasing",
      "changePercent": 12.45,
      "peakPeriod": "Month 5",
      "data": [65, 68, 72, 75, 78, 80, 82, 85, 87, 89, 91, 93, 94, 95, 96, 97, 98, 98, 99, 99, 100, 99, 98, 97]
    }
  ],
  "relatedTopics": [
    {
      "topic": "artificial intelligence",
      "relatedTopics": ["neural", "networks", "deep", "learning", "automation"]
    }
  ],
  "predictions": [
    {
      "topic": "artificial intelligence",
      "prediction": "artificial intelligence shows increasing interest. Expected to continue growing by 18.45% in the next period.",
      "confidence": 0.75,
      "timeframe": "next period",
      "factors": [
        "Current market trends",
        "Seasonal patterns",
        "Related topic performance",
        "Search volume patterns"
      ]
    }
  ],
  "timestamp": "2025-11-02T17:04:02.839Z"
}

9. Research Assistant (`research_assistant`)

Comprehensive research assistant with multi-step workflows, source synthesis, and structured report generation.

Parameters:

researchTopic (required): The main research topic or question to investigate (minimum 10 characters)
researchType (optional): Type of research to conduct - "academic", "news", "factual", "comprehensive" (default: "comprehensive")
depth (optional): Research depth level - "quick", "standard", "deep" (default: "standard")
sources (optional): Specific sources to include in research (max 15 sources)
excludeSources (optional): Sources to exclude from research (max 10 sources)
timeframe (optional): Time range for research results - "d1", "d7", "m1", "m6", "y1", "y2" (default: "y1")
maxSourcesPerType (optional): Maximum sources per source type (2-8, default: 5)
includeCitations (optional): Include detailed citations and source tracking (default: true)
generateReport (optional): Generate structured research report (default: true)
focusAreas (optional): Specific areas to focus research on (e.g., ["methodology", "findings", "implications"])

Research Types & Source Categories:

Academic: Academic journals, educational institutions, research repositories
News: News sources, fact checkers, international media
Factual: Government sources, scientific institutions, reference materials
Comprehensive: All source types for thorough research

Use Cases:

Academic research and literature reviews
Market research and competitive analysis
Policy research and government analysis
Scientific research and evidence synthesis
Business intelligence and strategic planning
News analysis and media monitoring
Fact-checking and verification workflows

Example:

{
  "name": "research_assistant",
  "arguments": {
    "researchTopic": "artificial intelligence impact on healthcare",
    "researchType": "comprehensive",
    "depth": "standard",
    "maxSourcesPerType": 3,
    "focusAreas": ["methodology", "findings", "implications"],
    "includeCitations": true,
    "generateReport": true,
    "timeframe": "y1"
  }
}

Response Format:

{
  "researchTopic": "artificial intelligence impact on healthcare",
  "researchType": "comprehensive",
  "depth": "standard",
  "timeframe": "y1",
  "maxSourcesPerType": 3,
  "includeCitations": true,
  "generateReport": true,
  "focusAreas": ["methodology", "findings", "implications"],
  "researchWorkflow": {
    "phase": "completed",
    "stepsCompleted": 5,
    "totalSteps": 5,
    "currentStep": "Research completed"
  },
  "sourceCategories": ["Academic", "News", "Government", "Reference", "Specialized"],
  "findings": [
    {
      "source": "nature.com",
      "category": "Academic",
      "url": "https://example.com/article",
      "title": "AI in Healthcare Research",
      "content": "Full extracted content...",
      "wordCount": 1250,
      "sentiment": {
        "score": 0.8,
        "comparative": 0.15
      },
      "relevanceScore": 0.9,
      "keyInsights": ["AI shows promise in diagnostic accuracy", "Implementation challenges remain"],
      "focusAnalysis": {
        "methodology": ["Randomized controlled trials", "Machine learning algorithms"],
        "findings": ["Improved diagnostic accuracy by 15%", "Reduced false positives"],
        "implications": ["Potential for widespread adoption", "Need for regulatory framework"]
      },
      "contentQualityScore": 0.85,
      "extractionTime": "2024-01-15T10:30:00.000Z"
    }
  ],
  "sources": [
    {
      "source": "nature.com",
      "category": "Academic",
      "resultCount": 3,
      "totalAvailable": "150",
      "results": [
        {
          "title": "AI in Healthcare Research",
          "link": "https://example.com/article",
          "snippet": "Artificial intelligence is transforming healthcare...",
          "displayLink": "nature.com",
          "relevanceScore": 0.9
        }
      ],
      "credibilityScore": 0.9
    }
  ],
  "citations": [
    {
      "title": "AI in Healthcare Research",
      "url": "https://example.com/article",
      "source": "nature.com",
      "category": "Academic",
      "credibilityScore": 0.9,
      "relevanceScore": 0.9,
      "accessedDate": "2024-01-15T10:30:00.000Z"
    }
  ],
  "synthesis": {
    "keyFindings": [
      "AI demonstrates significant potential in healthcare diagnostics",
      "Implementation faces regulatory and technical challenges",
      "Patient outcomes show measurable improvement with AI assistance"
    ],
    "conflictingInformation": [],
    "consensusPoints": [
      "AI technology shows promise in healthcare applications",
      "Regulatory frameworks need development for safe implementation"
    ],
    "gapsInKnowledge": [
      "Long-term impact studies are limited",
      "Cost-benefit analysis needs more research"
    ],
    "confidenceLevel": 0.85
  },
  "report": {
    "title": "Research Report: artificial intelligence impact on healthcare",
    "executiveSummary": "This research analyzed 15 sources across 5 categories...",
    "methodology": "Research methodology involved systematic search...",
    "findings": "Key findings from the research:\n1. AI shows promise...",
    "synthesis": "Synthesis of findings reveals 2 consensus points...",
    "recommendations": "High confidence in findings. Recommendations can be made...",
    "limitations": "Research limitations include: limited to publicly available sources...",
    "citations": [...],
    "metadata": {
      "generatedAt": "2024-01-15T10:30:00.000Z",
      "researchType": "comprehensive",
      "depth": "standard",
      "totalSources": 15,
      "confidenceLevel": 0.85,
      "qualityScore": 0.82
    }
  },
  "statistics": {
    "totalSourcesSearched": 15,
    "successfulSearches": 14,
    "failedSearches": 1,
    "totalResults": 45,
    "averageCredibilityScore": 0.87,
    "researchQualityScore": 0.82
  }
}

Research Workflow Phases:

Multi-source Research: Systematic search across categorized sources
Content Analysis: Extraction, sentiment analysis, and focus area analysis
Synthesis: Cross-reference analysis and consensus identification
Citation Management: Automated citation generation and tracking
Report Generation: Structured research report with executive summary

Quality Metrics:

Research Quality Score: Composite score based on source diversity, credibility, and findings quality
Confidence Level: Overall confidence in research findings based on source agreement
Source Diversity: Number of different source categories included
Content Quality: Assessment of extracted content relevance and depth

Available Resources

Google Search MCP Server provides 8 specialized resources that offer cached search results, trend analysis, and research data with intelligent caching for optimal performance:

`google://search/cache/{query}`

Returns cached Google search results for a query with metadata and timestamps.

Resource Details:

Purpose: Access recently cached search results without API calls
Benefits: Faster response times, reduced API usage, offline capability for recent searches
Cache TTL: 5 minutes - balances freshness with performance
Use Cases: Frequently accessed search terms, monitoring queries, development testing

Response Format:

{
  "query": "artificial intelligence",
  "results": [
    {
      "title": "Artificial Intelligence - Wikipedia",
      "link": "https://en.wikipedia.org/wiki/Artificial_intelligence",
      "snippet": "Artificial intelligence (AI) is intelligence demonstrated by machines..."
    }
  ],
  "searchTime": "0.25",
  "totalResults": "about 2,450,000,000",
  "cached": false,
  "timestamp": "2025-11-02T17:09:14.866Z"
}

`google://search/trends/{topic}`

Provides search interest trends and predictions for topics over time.

Resource Details:

Purpose: Analyze search interest patterns and predict trends
Benefits: Market research, content strategy, trend identification
Cache TTL: 5 minutes - keeps trend data reasonably current
Use Cases: SEO analysis, content planning, market research

Response Format:

{
  "topic": "machine learning",
  "trends": {
    "interest": [25, 30, 45, 60, 55, 70],
    "timeframe": "6M",
    "region": "US",
    "predictions": [75, 80, 85]
  },
  "cached": false,
  "timestamp": "2025-11-02T17:09:14.866Z"
}

`google://search/analytics/{query}`

Provides comprehensive search analytics including multiple results and patterns.

Resource Details:

Purpose: Deep analysis of search results and patterns
Benefits: Comprehensive search intelligence and pattern recognition
Cache TTL: 5 minutes - ensures analytical data stays relevant
Use Cases: Competitive analysis, keyword research, content optimization

Response Format:

{
  "query": "renewable energy",
  "analytics": {
    "totalResults": 1250000,
    "topDomains": ["wikipedia.org", "energy.gov", "iea.org"],
    "contentTypes": ["educational": 45, "commercial": 30, "news": 25],
    "sentiment": {"positive": 0.6, "neutral": 0.3, "negative": 0.1}
  },
  "cached": false,
  "timestamp": "2025-11-02T17:09:14.866Z"
}

`google://content/extracted/{url}`

Provides cached extracted content with sentiment analysis from web pages.

Resource Details:

Purpose: Access processed web content without re-extraction
Benefits: Faster content analysis, reduced processing overhead
Cache TTL: 5 minutes - balances content freshness with performance
Use Cases: Content monitoring, sentiment analysis, data extraction

Response Format:

{
  "url": "https://example.com/article",
  "content": {
    "title": "Article Title",
    "text": "Full article content...",
    "wordCount": 1250,
    "sentiment": {
      "score": 0.3,
      "comparative": 0.024,
      "tokens": ["article", "content", "analysis"],
      "words": ["good", "excellent"],
      "positive": ["good", "excellent"],
      "negative": []
    },
    "readability": 72.5
  },
  "cached": false,
  "timestamp": "2025-11-02T17:09:14.866Z"
}

`google://news/recent/{topic}`

Provides recent news articles with credibility analysis for topics.

Resource Details:

Purpose: Access recent news with quality filtering
Benefits: Time-sensitive information with credibility scoring
Cache TTL: 2 minutes - ensures news stays current
Use Cases: News monitoring, crisis management, current events tracking

Response Format:

{
  "topic": "climate change",
  "news": {
    "articles": [
      {
        "title": "New Climate Report Released",
        "source": "reuters.com",
        "credibility": 0.95,
        "published": "2025-11-02T15:30:00Z",
        "summary": "Latest IPCC report details..."
      }
    ],
    "totalArticles": 15,
    "avgCredibility": 0.87
  },
  "cached": false,
  "timestamp": "2025-11-02T17:09:14.866Z"
}

`google://academic/results/{query}`

Provides cached academic papers and research documents.

Resource Details:

Purpose: Access scholarly research without repeated searches
Benefits: Faster academic research, reduced API usage for research queries
Cache TTL: 30 minutes - academic content changes less frequently
Use Cases: Literature reviews, research planning, academic writing

Response Format:

{
  "query": "quantum computing",
  "results": {
    "papers": [
      {
        "title": "Advances in Quantum Computing",
        "authors": ["Dr. Jane Smith", "Dr. John Doe"],
        "journal": "Nature Physics",
        "year": 2025,
        "citations": 45,
        "doi": "10.1038/s41567-025-01234-5"
      }
    ],
    "totalPapers": 1250,
    "disciplines": ["Physics", "Computer Science", "Mathematics"]
  },
  "cached": false,
  "timestamp": "2025-11-02T17:09:14.866Z"
}

`google://research/summary/{topic}`

Provides comprehensive research summaries with sources and analysis.

Resource Details:

Purpose: Access complete research synthesis without reprocessing
Benefits: In-depth research insights with comprehensive analysis
Cache TTL: 1 hour - research summaries are stable over longer periods
Use Cases: Executive summaries, research reports, strategic planning

Response Format:

{
  "topic": "blockchain technology",
  "summary": {
    "executiveSummary": "Blockchain technology continues to evolve...",
    "keyFindings": [
      "Decentralized consensus mechanisms improving",
      "Enterprise adoption accelerating",
      "Regulatory frameworks emerging"
    ],
    "sourcesAnalyzed": 25,
    "confidenceLevel": 0.89,
    "methodology": "Multi-source analysis with peer review"
  },
  "cached": false,
  "timestamp": "2025-11-02T17:09:14.866Z"
}

`google://fact/check/{claim}`

Provides fact verification results with supporting evidence.

Resource Details:

Purpose: Access fact-checking results for claims
Benefits: Reliable fact verification with evidence trails
Cache TTL: 24 hours - facts don't change frequently
Use Cases: Content verification, journalism, educational fact-checking

Response Format:

{
  "claim": "The Earth is flat",
  "verification": {
    "verdict": "False",
    "confidence": 1.0,
    "evidence": [
      {
        "source": "NASA Scientific Consensus",
        "type": "Scientific Evidence",
        "strength": "Overwhelming",
        "summary": "Multiple independent measurements confirm spherical Earth"
      }
    ],
    "lastUpdated": "2025-11-01T10:00:00Z"
  },
  "cached": false,
  "timestamp": "2025-11-02T17:09:14.866Z"
}

Development

Development Commands

# Install dependencies
npm install

# Run in development mode with hot reload
npm run dev

# Build the project
npm run build

# Run tests
npm test

# Run linting
npm run lint

# Start production server
npm start

Project Structure

mcp-server-google-search/
├── dist/                    # Compiled JavaScript output
├── __tests__/              # Test files
│   └── mcp-server.test.ts  # MCP server tests
├── config.ts               # Configuration and environment variables
├── index.ts                # Main entry point
├── mcp-server.ts           # MCP server implementation with all 6 tools
├── package.json            # Dependencies and scripts
├── tsconfig.json           # TypeScript configuration
├── jest.config.js          # Jest testing configuration
├── global.d.ts             # TypeScript declarations
├── .env.example            # Environment variables template
├── example-config.json     # MCP configuration example
├── README.md               # This comprehensive documentation
└── LICENSE                 # MIT License

Technical Details

Language: TypeScript with ES modules
Runtime: Node.js 18+
Protocol: Model Context Protocol (MCP)
Validation: Zod schemas for all parameters
HTTP Client: Axios for API requests
HTML Parsing: Cheerio for content extraction
Sentiment Analysis: Sentiment library
Testing: Jest with TypeScript support

Error Handling

The server includes comprehensive error handling for:

API Authentication: Invalid Google API credentials
Network Issues: Timeouts, connection failures, rate limiting
Parameter Validation: Invalid search parameters and malformed requests
Content Extraction: Failed web page parsing and extraction
Rate Limiting: Google API quota exceeded
Malformed URLs: Invalid URLs for content extraction

Performance Features

Concurrent Requests: Parallel processing for multi-site searches
Error Recovery: Graceful degradation when individual sources fail
Response Caching: Efficient result aggregation and statistics
Memory Management: Optimized for long-running MCP server processes

License

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

Contributing

We welcome contributions! Here's how you can help:

Fork the repository on GitHub
Create a feature branch: git checkout -b feature/amazing-feature
Make your changes and add tests if applicable
Run the test suite: npm test
Commit your changes: git commit -m 'Add amazing feature'
Push to the branch: git push origin feature/amazing-feature
Open a Pull Request on GitHub

Areas for Contribution

New Tools: Add specialized search tools for specific domains
Enhanced Analytics: Improve search analytics and trend analysis
Performance: Optimize API calls and response times
Documentation: Improve examples and use cases
Testing: Add more comprehensive test coverage
Error Handling: Enhance error messages and recovery

Support

Getting Help

GitHub Issues: Open an issue for bugs or feature requests
Discussions: Use GitHub Discussions for questions and community support
Documentation: Check this README for comprehensive usage examples

Common Issues

API Key Issues: Ensure your Google API key is valid and has Custom Search API enabled
CSE ID Problems: Verify your Custom Search Engine ID is correct
Rate Limiting: Google API has daily quotas - check your usage in Google Cloud Console
MCP Client Issues: Restart your MCP client after configuration changes

Useful Links

About

This MCP server transforms Google's powerful search capabilities into intelligent AI tools, enabling seamless integration with modern AI assistants. Built with TypeScript and following MCP standards, it provides a robust foundation for search-powered AI applications.

Repository: https://github.com/1999AZZAR/mcp-server-google-search

Created by: 1999AZZAR

License: MIT

Made with dedication for the AI community

1999AZZAR/Google-Search-MCP

Google Search MCP Server

Table of Contents

Features

Prerequisites

Installation

Configuration

Environment Variables

Getting Google API Credentials

Step 1: Google Cloud Console Setup

Step 2: Custom Search Engine Setup

Step 3: Configure Search Engine (Optional)

Security Best Practices

Usage

As an MCP Server

For Cursor IDE

For Claude Desktop

For Other MCP Clients

Testing the Server

Available Tools

1. Google Search (google_search)

2. Extract Content (extract_content)

3. Search Analytics (search_analytics)

4. Multi-Site Search (multi_site_search)

5. News Monitor (news_monitor)

6. Academic Search (academic_search)

7. Content Summarizer (content_summarizer)

8. Fact Checker (fact_checker)

9. Research Assistant (research_assistant)

10. Search Trends (search_trends)

9. Research Assistant (research_assistant)

Available Resources

google://search/cache/{query}

google://search/trends/{topic}

google://search/analytics/{query}

google://content/extracted/{url}

google://news/recent/{topic}

google://academic/results/{query}

google://research/summary/{topic}

google://fact/check/{claim}

Development

Development Commands

Project Structure

Technical Details

Error Handling

Performance Features

License

Contributing

Areas for Contribution

Support

Getting Help

Common Issues

Useful Links

About

1. Google Search (`google_search`)

2. Extract Content (`extract_content`)

3. Search Analytics (`search_analytics`)

4. Multi-Site Search (`multi_site_search`)

5. News Monitor (`news_monitor`)

6. Academic Search (`academic_search`)

7. Content Summarizer (`content_summarizer`)

8. Fact Checker (`fact_checker`)

9. Research Assistant (`research_assistant`)

10. Search Trends (`search_trends`)

9. Research Assistant (`research_assistant`)

`google://search/cache/{query}`

`google://search/trends/{topic}`

`google://search/analytics/{query}`

`google://content/extracted/{url}`

`google://news/recent/{topic}`

`google://academic/results/{query}`

`google://research/summary/{topic}`

`google://fact/check/{claim}`