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marcopesani

serper-search-scrape-mcp-server

by marcopesani
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TypeScript
Remote

Serper Search and Scrape MCP Server

A TypeScript-based MCP server that provides web search and webpage scraping capabilities using the Serper API. This server integrates with Claude Desktop to enable powerful web search and content extraction features.

Features

Tools

  • google_search - Perform web searches via Serper API

    • Rich search results including organic results, knowledge graph, "people also ask", and related searches

    • Supports region and language targeting

    • Optional parameters for location, pagination, time filters, and autocorrection

    • Supports advanced search operators:

      • site: Limit results to specific domain

      • filetype: Limit to specific file types (e.g., 'pdf', 'doc')

      • inurl: Search for pages with word in URL

      • intitle: Search for pages with word in title

      • related: Find similar websites

      • cache: View Google's cached version of a specific URL

      • before: Date before in YYYY-MM-DD format

      • after: Date after in YYYY-MM-DD format

      • exact: Exact phrase match

      • exclude: Terms to exclude from search results

      • or: Alternative terms (OR operator)

  • scrape - Extract content from web pages

    • Get plain text and optional markdown content

    • Includes JSON-LD and head metadata

    • Preserves document structure

Related MCP server: go_serper_mcp_server

Requirements

  • Node.js >= 18

  • Serper API key (set as SERPER_API_KEY environment variable)

Development

Install dependencies:

npm install

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

Run tests:

npm test                  # Run all tests
npm run test:watch       # Run tests in watch mode
npm run test:coverage    # Run tests with coverage
npm run test:integration # Run integration tests

Environment Variables

Create a .env file in the root directory:

SERPER_API_KEY=your_api_key_here

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

Installation

Installing via Smithery

To install Serper Search and Scrape for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @marcopesani/mcp-server-serper --client claude

Claude Desktop

Add the server config at:

  • MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "serper-search": {
      "command": "npx",
      "args": ["-y", "serper-search-scrape-mcp-server"],
      "env": {
        "SERPER_API_KEY": "your_api_key_here"
      }
    }
  }
}

Cline

  1. Open the Cline extension settings

  2. Open "MCP Servers" tab

  3. Click on "Configure MCP Servers"

  4. Add the server config:

{
  "mcpServers": {
    "github.com/marcopesani/mcp-server-serper": {
      "command": "npx",
      "args": ["-y", "serper-search-scrape-mcp-server"],
      "env": {
        "SERPER_API_KEY": "your_api_key_here"
      },
      "disabled": false,
      "autoApprove": ["google_search", "scrape"]
    }
  }
}

Additional Cline configuration options:

  • disabled: Set to false to enable the server

  • autoApprove: List of tools that don't require explicit approval for each use

Cursor

  1. Open the Cursor settings

  2. Open "Features" settings

  3. In the "MCP Servers" section, click on "Add new MCP Server"

  4. Choose a name, and select "command" as "Type"

  5. In the "Command" field, enter the following:

env SERPER_API_KEY=your_api_key_here npx -y serper-search-scrape-mcp-server

Docker

You can also run the server using Docker. First, build the image:

docker build -t mcp-server-serper .

Then run the container with your Serper API key:

docker run -e SERPER_API_KEY=your_api_key_here mcp-server-serper

Alternatively, if you have your environment variables in a .env file:

docker run --env-file .env mcp-server-serper

For development, you might want to mount your source code as a volume:

docker run -v $(pwd):/app --env-file .env mcp-server-serper

Note: Make sure to replace your_api_key_here with your actual Serper API key.

Available Tools

2 tools
scrapeC

Tool to scrape a webpage and retrieve the text and, optionally, the markdown content. It will retrieve also the JSON-LD metadata and the head metadata.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL of the webpage to scrape.
includeMarkdownNoWhether to include markdown content.

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool retrieves (text, markdown, JSON-LD, head metadata) but lacks critical behavioral details such as rate limits, authentication needs, error handling, or whether it performs destructive actions (e.g., modifying data). For a web scraping tool with no annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded in a single sentence, efficiently stating the core functionality without unnecessary details. Every part earns its place, though it could be slightly more structured (e.g., separating outputs). It avoids redundancy and is appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (web scraping with two parameters), no annotations, and no output schema, the description is partially complete. It covers what the tool retrieves but misses behavioral aspects like rate limits or error handling. Without an output schema, it should ideally describe return values more explicitly, but it does list the types of content retrieved, providing some context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, clearly documenting both parameters ('url' and 'includeMarkdown'). The description adds no additional meaning beyond the schema—it doesn't explain parameter interactions, constraints, or usage examples. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'scrape a webpage and retrieve the text and, optionally, the markdown content. It will retrieve also the JSON-LD metadata and the head metadata.' This specifies the verb (scrape), resource (webpage), and outputs (text, markdown, JSON-LD, head metadata). However, it doesn't explicitly differentiate from the sibling tool 'google_search', which likely serves a different purpose (searching vs. scraping).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'google_search' or any other scraping-related tools, nor does it specify prerequisites, contexts, or exclusions for usage. This leaves the agent without clear direction on tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.3/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: google_search is for performing web searches and retrieving search results, while scrape is for extracting content from a specific webpage. There is no overlap in functionality, making it easy for an agent to choose the right tool for each task.

Naming Consistency5/5

Both tools follow a consistent naming pattern using snake_case with descriptive, action-oriented names (google_search and scrape). The naming is straightforward and predictable, with no deviations or mixed conventions.

Tool Count3/5

With only 2 tools, the server feels thin for a search-and-scrape domain, as it might lack additional utilities like batch processing, advanced filtering, or result summarization. However, the core functions are covered, making it borderline but functional.

Completeness4/5

The tools cover the essential workflows for search and scraping: performing searches and extracting webpage content. Minor gaps exist, such as no tool for managing search history or handling pagination, but agents can work around these with the provided tools.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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