A
licenseB
qualityD
maintenanceProvides web search capabilities using Google Custom Search API, enabling users to perform searches through a Model Context Protocol server.
2
255 npm
69
MIT
MCP Server for Google Search
by mcp-for-dev
用于 Google 搜索的 MCP 服务器
使用 Google 自定义搜索 API 和网页内容提取功能提供网络搜索功能的模型上下文协议服务器。
工具
搜索
使用 Google 自定义搜索 API 执行网络搜索：
搜索整个网络或特定网站
控制结果数量（1-10）
通过标题、链接和摘要获取结构化结果
网页阅读器
从任何网页提取内容：
获取并解析网页内容
提取页面标题和正文
通过删除脚本和样式来清理内容
返回包含标题、文本和 URL 的结构化数据
Related MCP server: MCP Google Custom Search Server
安装
获取 Google API 密钥和搜索引擎 ID
创建 Google Cloud 项目：
创建新项目或选择现有项目
为您的项目启用计费
启用自定义搜索 API：
前往API 库
搜索“自定义搜索 API”
点击“启用”
获取 API 密钥：
前往凭证
点击“创建凭证”>“API 密钥”
复制您的 API 密钥
（可选）将 API 密钥限制为仅限自定义搜索 API
创建自定义搜索引擎：
前往可编程搜索引擎
输入您要搜索的网站（使用www.google.com进行常规网页搜索）
点击“创建”
在下一页上，点击“自定义”
在设置中，启用“搜索整个网络”
复制您的搜索引擎 ID (cx)
客户端配置
要与 Claude Desktop 一起使用，请使用您的 Google API 凭据添加服务器配置：
在 MacOS 上： ~/Library/Application Support/Claude/claude_desktop_config.json在 Windows 上： %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 toolsgoogle_searchC
Perform a web search query
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| num | No | Number of results (1-10) |
TDQS
C2.8/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose any behavioral traits such as rate limits, caching, or return format. The agent is left without important context for safe invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, but it is too minimal. While there is no wasted text, it lacks structure (e.g., separating purpose from usage details).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple structure (2 parameters, no output schema), the description fails to mention return behavior or result format, leaving the agent unaware of what to expect after invocation.
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 for both parameters, so the schema itself provides the meaning. The description adds no further semantic value beyond restating the schema.
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 'Perform a web search query' clearly indicates the tool's verb and resource, distinguishing it from the sibling 'read_webpage' which reads a specific page.
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?
No guidance on when to use this tool vs. alternatives (e.g., read_webpage) or any prerequisites. The description lacks explicit usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_webpageA
Fetch and extract text content from a webpage
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the webpage to read |
TDQS
A3.5/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It only states basic purpose without mentioning rate limits, authentication, dynamic content handling, or error responses.
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?
Single sentence, front-loaded with action, no unnecessary words. Perfectly concise.
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?
For a simple tool with one parameter and no output schema, the description is adequate but lacks details on handling of large pages, timeouts, or what 'text content' entails (e.g., stripping HTML).
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 coverage is 100% (single 'url' parameter described), so baseline is 3. The description adds no extra meaning beyond the schema, such as URL format or protocol support.
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?
Description clearly states the action ('Fetch and extract') and the resource ('text content from a webpage'), distinguishing it from sibling tool 'search' which is for querying.
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?
No explicit guidance on when to use this tool versus alternatives. The sibling 'search' suggests a different purpose, but the description does not clarify contexts or exclusions.
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.
2 tool updates
v1.0.0- Added
google_search - Added
read_webpage
TDQS
A3.5/5.0
Scored across 2 tools
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.
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