Google Search MCP Server (SERP)

A hosted Model Context Protocol (MCP) server that gives Claude, Cursor, Windsurf and any other MCP client eight read-only Google Search tools. Pull the live SERP with its AI Overview and People Also Ask, run a Google AI Mode query, and read news, shopping, product detail and short-video results, all as structured JSON, with no Google Cloud project and no search-engine setup.

https://mcp.hasdata.com/api/mcp?apis=google_serp

"SERP" and "Google Search" are the same product here. This server returns Google search-engine results pages, parsed.

Contents

• What you need
• Quick start
• Example prompts
• Tools
• Errors and failure paths
• Pricing, free tier and limits
• Tool selection
• How it compares
• FAQ
• HasData links
• Development
• Contributing
• License

What you need

An MCP client that speaks streamable HTTP with custom headers. A HasData API key from the dashboard, free to create with no card, and the trial covers about 100 to 200 calls depending on the tool. Nothing else. This is a remote server, so the simplest path is a URL and a header, with no Google Cloud project or Programmable Search Engine to set up. A stdio-only client can use the @hasdata/google-search-mcp (npm) or hasdata-google-search-mcp (PyPI) launcher instead.

Quick start

The server URL is the same for every client. We run it hands-on in Claude Code and Claude Desktop. The other blocks follow each client's own documented format for a remote server.

Clients with OAuth support can add the same URL as a connector and sign in without putting a key in a config file.

claude mcp add --transport http google-search "https://mcp.hasdata.com/api/mcp?apis=google_serp" \
  --header "x-api-key: HASDATA_API_KEY"

{
  "mcpServers": {
    "google-search": {
      "command": "npx",
      "args": ["-y", "@hasdata/google-search-mcp"],
      "env": { "HASDATA_API_KEY": "YOUR_KEY" }
    }
  }
}

{
  "mcpServers": {
    "google-search": {
      "command": "uvx",
      "args": ["hasdata-google-search-mcp"],
      "env": { "HASDATA_API_KEY": "YOUR_KEY" }
    }
  }
}

{
  "mcpServers": {
    "google-search": {
      "url": "https://mcp.hasdata.com/api/mcp?apis=google_serp",
      "headers": { "x-api-key": "HASDATA_API_KEY" }
    }
  }
}

{
  "mcpServers": {
    "google-search": {
      "serverUrl": "https://mcp.hasdata.com/api/mcp?apis=google_serp",
      "headers": { "x-api-key": "HASDATA_API_KEY" }
    }
  }
}

{
  "mcpServers": {
    "google-search": {
      "url": "https://mcp.hasdata.com/api/mcp?apis=google_serp",
      "type": "streamableHttp",
      "headers": { "x-api-key": "HASDATA_API_KEY" },
      "disabled": false
    }
  }
}

{
  "servers": {
    "google-search": {
      "type": "http",
      "url": "https://mcp.hasdata.com/api/mcp?apis=google_serp",
      "headers": { "x-api-key": "HASDATA_API_KEY" }
    }
  }
}

{
  "mcpServers": {
    "google-search": {
      "httpUrl": "https://mcp.hasdata.com/api/mcp?apis=google_serp",
      "headers": { "x-api-key": "HASDATA_API_KEY" }
    }
  }
}

Example prompts

Search Google for best running shoes and give me the organic top ten plus the AI Overview.

One call, 10 credits. The SERP response carries the AI Overview inline alongside the organic results.

For the same query, take each People Also Ask question and pull its AI Overview answer with sources.

One call per question, 5 credits each. Each relatedQuestions entry holds an aiOverview.pageToken, and the AI Overview tool turns that token into the answer blocks and their references.

Ask Google AI Mode what is the Model Context Protocol and give me the answer with its citations.

One call, 10 credits. AI Mode returns the generated answer as text blocks with a reference list.

Search Google Shopping for nike air max, then pull the full product card for the top result: every store selling it, the price range and the review breakdown.

Two calls. Shopping is 10 credits and returns a token per product, and the immersive product tool spends 5 to expand that token into stores, variants and reviews.

Get the latest Google News for artificial intelligence, and separately the short-video results for cooking pasta.

Two calls, 10 credits each.

The workflow leans on two chains. A SERP response hands back an aiOverview inline and a pageToken on every People Also Ask question, so extracting Google's generative answers is either free with the search or one 5-credit follow-up per question. A shopping result likewise hands back a token per product, so the jump from a listing to its full multi-store card is a single call.

Tools

Eight tools, all read-only. Samples below are trimmed from real calls, and the results in them change as Google changes, so read them as shapes. Each tool name links to its endpoint reference.

The samples are the payload, not the whole response. A tools/call result carries one text block, and that text is itself JSON holding url, status, text and json, with the scraped data under json. From a raw JSON-RPC response the path is result.content[0].text, parsed, then .json. A chat client unwraps that for you and code talking to the endpoint directly does not.

Google SERP

hasdata_google_serp_serp_getSearchResults

The full results page for a query.

Returns searchInformation, organicResults, aiOverview, relatedQuestions, relatedSearches, perspectives, immersiveProducts and pagination, with whichever blocks Google shows for the query. Organic entries carry position, title, link, displayedLink, source, snippet, snippetHighlitedWords, date and images.

The AI Overview arrives two ways. Usually aiOverview is inline, with textBlocks and references you can read straight away. Sometimes Google gates it behind a token, and then aiOverview carries a pageToken and a hasdataLink instead of the blocks. Every relatedQuestions entry is that second case too. It holds a question and an aiOverview with the same pageToken and hasdataLink, which the AI Overview tool below expands. So the People Also Ask answers are AI Overviews you fetch one token at a time. The top-level aiOverview is inline on most queries and a token on a few, so read it both ways.

Read the full README on GitHub →