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
MCP-Server für die Google-Suche
Ein Model Context Protocol-Server, der Websuchfunktionen mithilfe der Google Custom Search API und einer Funktion zum Extrahieren von Webseiteninhalten bereitstellt.
Werkzeuge
Suchen
Führen Sie Websuchen mit der Google Custom Search API durch:
Durchsuchen Sie das gesamte Web oder bestimmte Websites
Kontrollanzahl der Ergebnisse (1-10)
Erhalten Sie strukturierte Ergebnisse mit Titel, Link und Snippet
Webseiten-Reader
Extrahieren Sie Inhalte von jeder Webseite:
Abrufen und Analysieren von Webseiteninhalten
Seitentitel und Haupttext extrahieren
Bereinigen Sie Inhalte, indem Sie Skripte und Stile entfernen
Strukturierte Daten mit Titel, Text und URL zurückgeben
Related MCP server: MCP Google Custom Search Server
Installation
Holen Sie sich den Google API-Schlüssel und die Suchmaschinen-ID
Erstellen Sie ein Google Cloud-Projekt:
Gehen Sie zur Google Cloud Console
Erstellen Sie ein neues Projekt oder wählen Sie ein vorhandenes aus
Aktivieren Sie die Abrechnung für Ihr Projekt
Aktivieren Sie die benutzerdefinierte Such-API:
Zur API-Bibliothek
Suche nach „Custom Search API“
Klicken Sie auf „Aktivieren“
API-Schlüssel abrufen:
Gehe zu Anmeldeinformationen
Klicken Sie auf „Anmeldeinformationen erstellen“ > „API-Schlüssel“.
Kopieren Sie Ihren API-Schlüssel
(Optional) Beschränken Sie den API-Schlüssel auf die benutzerdefinierte Such-API
Benutzerdefinierte Suchmaschine erstellen:
Geben Sie die Sites ein, die Sie durchsuchen möchten (verwenden Sie www.google.com für die allgemeine Websuche)
Klicken Sie auf „Erstellen“
Klicken Sie auf der nächsten Seite auf „Anpassen“
Aktivieren Sie in den Einstellungen „Im gesamten Web suchen“
Kopieren Sie Ihre Suchmaschinen-ID (cx)
Client-Konfiguration
Zur Verwendung mit Claude Desktop fügen Sie die Serverkonfiguration mit Ihren Google-API-Anmeldeinformationen hinzu:
Unter MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json Unter 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 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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