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mcp-for-dev

MCP Server for Google Search

by mcp-for-dev
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MCP Server for Google Search

A Model Context Protocol server that provides web search capabilities using Google Custom Search API and webpage content extraction functionality.

Tools

Search

Perform web searches using Google Custom Search API:

  • Search the entire web or specific sites

  • Control number of results (1-10)

  • Get structured results with title, link, and snippet

Webpage Reader

Extract content from any webpage:

  • Fetch and parse webpage content

  • Extract page title and main text

  • Clean content by removing scripts and styles

  • Return structured data with title, text, and URL

Related MCP server: MCP Google Custom Search Server

Installation

Get Google API Key and Search Engine ID

  1. Create a Google Cloud Project:

    • Go to Google Cloud Console

    • Create a new project or select an existing one

    • Enable billing for your project

  2. Enable Custom Search API:

    • Go to API Library

    • Search for "Custom Search API"

    • Click "Enable"

  3. Get API Key:

    • Go to Credentials

    • Click "Create Credentials" > "API Key"

    • Copy your API key

    • (Optional) Restrict the API key to only Custom Search API

  4. Create Custom Search Engine:

    • Go to Programmable Search Engine

    • Enter the sites you want to search (use www.google.com for general web search)

    • Click "Create"

    • On the next page, click "Customize"

    • In the settings, enable "Search the entire web"

    • Copy your Search Engine ID (cx)

Client Configuration

To use with Claude Desktop, add the server config with your Google API credentials:

On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "google-search": {
      "command": "npx",
      "args": ["-y", "@mcp-for-dev/mcp-google-search"],
      "env": {
        "GOOGLE_API_KEY": "your-api-key-here",
        "GOOGLE_SEARCH_ENGINE_ID": "your-search-engine-id-here"
      }
    }
  }
}

Available Tools

2 tools
read_webpageA

Fetch and extract text content from a webpage

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the webpage to read

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the basic action (fetch and extract text) but does not mention potential limitations like JavaScript execution, timeouts, or content size limits. For a simple tool, this is adequate but not thorough.

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

Conciseness5/5

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

The description is a single sentence that is concise and to the point, with no unnecessary words. It efficiently conveys the tool's purpose.

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

Completeness4/5

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

Given the tool's simplicity (one parameter, no output schema, no nested objects), the description is largely complete. It explains the input and the intended output. A minor addition could be mentioning that only text content is extracted, but it is not essential.

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?

Schema description coverage is 100% for the single parameter 'url,' and the description adds no further semantic information beyond what the schema already provides. Thus, it meets the baseline.

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

Purpose5/5

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

The description clearly states the tool's function: 'Fetch and extract text content from a webpage.' It uses a specific verb and resource, and it clearly distinguishes from the sibling tool 'google_search,' which performs a different task.

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

Usage Guidelines3/5

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

The description implies usage (read a specific webpage) but does not explicitly state when to use this tool versus the sibling 'google_search' or when not to use it. No alternative tools or exclusions are mentioned.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv1.0.0
    • Addedgoogle_search
    • Addedread_webpage
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TDQS

A3.5/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one performs web searches, the other extracts text from a specific URL. There is no overlap or ambiguity.

Naming Consistency5/5

Both tool names follow the same verb_noun pattern with snake_case (google_search, read_webpage), making them predictable and consistent.

Tool Count4/5

With only two tools, the set is minimal but covers the core search workflow. It avoids unnecessary bloat, though additional search variants could be justified.

Completeness4/5

The surface covers the basic search-then-read workflow. Missing features like pagination or filtered searches are minor gaps, but the essential path is complete.

Resources

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Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • MCP Google Server

    adenot
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    Provides web search capabilities using Google Custom Search API, enabling users to perform searches through a Model Context Protocol server.
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  • MCP Google Custom Search

    limklister
    A
    license
    B
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    A Model Context Protocol server that enables LLMs to perform web searches using Google's Custom Search API through a standardized interface.
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