googlesearch-mcp
Provides Google search capabilities, allowing users to search the web and retrieve results including titles, URLs, and descriptions.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@googlesearch-mcpsearch for latest AI news"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
googlesearch-mcp
deprecated , A New tool will emerge !
A Model Context Protocol server that exposes Google Search as a tool.
It uses the googlesearch-python library, which scrapes Google directly — no API key, no billing, no setup.
Features
🔍 Google search exposed as a single MCP tool.
📋 Returns title, URL and description for each result.
🌐 Configurable language, result count, deduplication and safe-search.
🚫 No API key required.
🖥️ Runs over stdio — works out of the box with Claude Desktop and other MCP clients.
Related MCP server: Serper MCP Server
Installation
No build step is required for the end user. Choose one of the configs below and
paste it into your MCP client's config file. The first invocation downloads the
package automatically (just like npx -y).
Quick start — paste into your MCP client config
Option A: uvx (recommended — Python's npx equivalent, no install)
For Claude Desktop's claude_desktop_config.json, VS Code / Kilo mcp.json, etc.:
{
"mcpServers": {
"search": {
"command": "uvx",
"args": ["googlesearch-mcp"]
}
}
}Requires uv installed (
curl -LsSf https://astral.sh/uv/install.sh | shon macOS/Linux).uvxruns the latest published version straight from PyPI with zero install.
Option B: from a git repo (no PyPI publish needed)
{
"mcpServers": {
"search": {
"command": "uvx",
"args": ["--from", "git+https://github.com/SkillfulElectro/googlesearch-mcp", "googlesearch-mcp"]
}
}
}Option C: pipx run (PyPI, no install)
{
"mcpServers": {
"search": {
"command": "pipx",
"args": ["run", "googlesearch-mcp"]
}
}
}Option D: from source (after pip install . or uv pip install .)
{
"mcpServers": {
"search": {
"command": "googlesearch-mcp"
}
}
}If your client cannot find googlesearch-mcp on PATH, point it at the module form:
{
"mcpServers": {
"search": {
"command": "python",
"args": ["-m", "googlesearch_mcp"]
}
}
}From source (this repo)
uv pip install . # or: pip install .
googlesearch-mcp # now on your PATHTool: search
Search Google and return a list of web results.
Parameter | Type | Default | Description |
| string | — | The search query (required). |
| int |
| Number of results to return. |
| string |
| Language code, e.g. |
| bool |
| Deduplicate result URLs. |
| string |
|
|
Each result is an object:
{
"index": 1,
"title": "Example Title",
"url": "https://example.com",
"description": "Snippet of the page…"
}Development
pip install -e .
mcp run googlesearch_mcp.server:appInspect the server with the MCP Inspector:
npx -y @modelcontextprotocol/inspector googlesearch-mcpNotes
googlesearch-pythonworks by scraping Google. Heavy use may trigger rate limits or CAPTCHAs. If you need a reliable production search backend, consider a paid API (e.g. SerpApi).The server runs on the stdio transport by default, which is what MCP clients expect.
Publishing a release
Releases are automated via GitHub Actions (.github/workflows/publish.yml):
Bump
versioninpyproject.toml(e.g.0.1.1).Commit and create a Git tag matching the version:
git tag v0.1.1 && git push --tags.In GitHub, publish a Release from that tag (or push the tag manually).
The
publishworkflow builds the wheel + sdist and uploads to PyPI using Trusted Publishing (OIDC) — no PyPI token needed in secrets.
One-time setup for trusted publishing (see https://docs.pypi.org/trusted-publishers):
Create the project on PyPI first (or claim
googlesearch-mcp).Add a publisher: environment
release, repoSkillfulElectro/googlesearch-mcp, workflow filenamepublish.yml.In the GitHub repo, create the
releaseenvironment and addid-token: writepermission — already set in the workflow below.
If you prefer a PyPI API token instead of trusted publishing, add a secret
PYPI_API_TOKEN and swap the publish job to use password: ${{ secrets.PYPI_API_TOKEN }}.
Available Tools
1 toolsearchA
Search Google and return web results. Powered by the googlesearch-python library (no API key needed).
Args: query: The search query. num_results: How many results to return (default 10). lang: Language code for results, e.g. "en", "fr", "de" (default "en"). unique: Deduplicate result URLs when True (default False). safe: Safe-search filter. Use "active" to enable, "" to disable (default "active").
Returns: A list of result objects with keys: index, title, url, description.
Raises:
ValueError: If query is empty/blank or num_results is not positive.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | en | |
| safe | No | active | |
| query | Yes | ||
| unique | No | ||
| num_results | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: it uses a specific library, returns a list of results with detailed fields, and raises ValueError for invalid inputs. It also explains parameters like deduplication and safe search.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise yet complete, with clear sections (Args, Returns, Raises). Every sentence adds value, and the main purpose is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the 5 parameters and presence of an output schema, the description covers all necessary context: parameter semantics, return format, error conditions. It is fully self-contained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must provide all parameter info. It does so comprehensively, listing each parameter with default values and usage context (e.g., 'lang' as language code, 'unique' for deduplication).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches Google and returns web results, using specific verbs and resources. It is unambiguous and distinct from any potential siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use this tool (for web search) and highlights a key benefit (no API key needed). However, it does not explicitly mention when not to use it or provide alternatives, though no siblings exist.
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.
1 tool update
v0.1.0- First observed
search
TDQS
Scored across 1 tool
Only one tool exists, so there is no possibility of confusion or overlap. An agent can clearly select the search tool without ambiguity.
The single tool is named 'search', which is a clear, concise verb that directly describes its purpose. There are no other tools to create inconsistency.
With only one tool, the server is minimal. While a single search tool can be sufficient for basic web search functionality, the scope feels slightly thin compared to many MCP servers that offer multiple related operations.
The search tool covers the core domain of web search with configurable parameters (num_results, lang, unique, safe). It lacks advanced features like image or news search, but for its stated purpose of returning web results, it is reasonably complete.
Maintenance
Related MCP Connectors
MCP server for Google search results via SERP API
Search remote Model Context Protocol servers and tools discovered by BuiltWith, without an API key.
A Model Context Protocol server for Wix AI tools
Serper MCP — wraps the Serper Google Search API (serper.dev)
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