by serpapi
SerpApi MCP Server for Google and other search engine results
# Add to your Claude Code skills
git clone https://github.com/serpapi/serpapi-mcpGuides for using mcp servers skills like serpapi-mcp.
Last scanned: 9/16/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-09-16T09:08:14.570Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how serpapi-mcp compares with popular alternatives.
serpapi-mcp is an open-source mcp servers skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by serpapi. SerpApi MCP Server for Google and other search engine results. It has 179 GitHub stars.
Yes. serpapi-mcp passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/serpapi/serpapi-mcp" and add it to your Claude Code skills directory (see the Installation section above).
serpapi-mcp is primarily written in Python. It is open-source under serpapi on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other MCP Servers skills you can browse and compare side by side. Open the MCP Servers category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh serpapi-mcp against similar tools.
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A Model Context Protocol (MCP) server implementation that integrates with SerpApi for comprehensive search engine results and data extraction.
search_table and search_dashboard tools that render results as an interactive UI in supporting hosts.mcpb), see belowSerpApi MCP Server is available as a hosted service at mcp.serpapi.com. In order to connect to it, you need to provide an API key. You can find your API key on your SerpApi dashboard.
You can configure Claude Desktop to use the hosted server:
{
"mcpServers": {
"serpapi": {
"type": "http",
"url": "https://mcp.serpapi.com/YOUR_SERPAPI_API_KEY/mcp"
}
}
}
You can also add the hosted server to these MCP clients:
OpenClaw
openclaw mcp add serpapi --url https://mcp.serpapi.com/YOUR_SERPAPI_API_KEY/mcp --transport streamable-http
Claude Code
claude mcp add --transport http serpapi https://mcp.serpapi.com/mcp --header "Authorization: Bearer YOUR_SERPAPI_API_KEY"
Hermes
hermes mcp add serpapi --url https://mcp.serpapi.com/YOUR_SERPAPI_API_KEY/mcp
Codex (reads the key from SERPAPI_API_KEY in your shell)
codex mcp add serpapi --url https://mcp.serpapi.com/mcp --bearer-token-env-var SERPAPI_API_KEY
git clone https://github.com/serpapi/serpapi-mcp.git
cd serpapi-mcp
uv sync && uv run src/server.py
Configure Claude Desktop:
{
"mcpServers": {
"serpapi": {
"type": "http",
"url": "http://localhost:8000/YOUR_SERPAPI_API_KEY/mcp"
}
}
}
Get your API key: serpapi.com/manage-api-key
For a local, one-click install, download the .mcpb bundle from the latest release (or build it as below) and open it with Claude Desktop (or drop it onto Settings → Extensions). Claude Desktop asks for your SerpApi API key during install, stores it as a sensitive setting, and runs the server locally over stdio. The bundle uses the MCPB uv runtime: it ships only the source, pyproject.toml and uv.lock, and Claude Desktop provisions Python and the locked dependencies with uv at install time, so nothing is vendored and one bundle works on macOS, Windows and Linux.
uv run mcpb/build.py # needs Node.js for the MCPB CLI; writes dist/serpapi-mcp-<version>.mcpb
Everything bundle-related lives in mcpb/, plus .mcpbignore at the project root. The build regenerates the engine schemas from the SerpApi Playground (--no-rebuild-engines bundles engines/ from the working tree instead), validates mcpb/manifest.json, packs the git-tracked files minus .mcpbignore with the manifest at the bundle root, then installs it into a temp dir and starts it over stdio to make sure it works (--no-smoke skips that last step). The bundle is only built at release time: pushing a v<version> tag runs the release workflow, which runs the test suite and then deploys the hosted server, publishes the MCP Registry entry, and builds the bundle and attaches it to the GitHub release. Pull requests run the manifest and stdio entry point tests in tests/test_mcpb.py but do not pack a bundle.
The same stdio entry point works with any local MCP host that launches servers as a subprocess:
{
"mcpServers": {
"serpapi": {
"command": "uv",
"args": ["run", "--directory", "/path/to/serpapi-mcp", "--frozen", "--no-dev", "src/stdio.py"],
"env": { "SERPAPI_API_KEY": "YOUR_SERPAPI_API_KEY" }
}
}
}
Two methods are supported:
Authorization: Bearer YOUR_API_KEY (recommended: the key stays out of URLs and logs)/YOUR_API_KEY/mcp, for clients that cannot set headersExamples:
# Header-based
curl "https://mcp.serpapi.com/mcp" -H "Authorization: Bearer your_key" -d '...'
# Path-based
curl "https://mcp.serpapi.com/your_key/mcp" -d '...'
No key is needed to connect, list tools or read resources. search and the App tools need one and return an error without it.
The MCP server has one main Search Tool that supports all SerpApi engines and result types. You can find all available parameters on the SerpApi API reference.
Engine parameter schemas are also exposed as MCP resources: serpapi://engines (index) and serpapi://engines/<engine>.
Clients that support argument completion can request engine-name suggestions for serpapi://engines/{engine_name}. For example, the prefix google_f suggests matching engine identifiers. This completes the resource URI parameter, not arbitrary search queries.
The parameters you can provide are specific for each API engine. Some sample parameters are provided below:
params.q (required): Search queryparams.engine: Search engine (default: "google_light")params.location: Geographic filterparams.output: Response format; omit for JSON (default), or set to "md" for Markdownmode: Response mode; "compact" removes metadata from JSON, while Markdown is returned unchangedExamples:
{"name": "search", "arguments": {"params": {"q": "coffee shops", "location": "Austin, TX"}}}
{"name": "search", "arguments": {"params": {"q": "weather in London"}}}
{"name": "search", "arguments": {"params": {"q": "AAPL stock"}}}
{"name": "search", "arguments": {"params": {"q": "news"}, "mode": "compact"}}
{"name": "search", "arguments": {"params": {"q": "detailed search"}, "mode": "complete"}}
{"name": "search", "arguments": {"params": {"q": "news", "output": "md"}}}
{"name": "search", "arguments": {"params": {"engine": "amazon", "k": "mechanical keyboards", "amazon_domain": "amazon.com", "output": "md"}}}
{"name": "search", "arguments": {"params": {"engine": "google_scholar", "q": "retrieval augmented generation"}}}
{"name": "search", "arguments": {"params": {"engine": "youtube", "search_query": "how to make espresso"}}}
{"name": "search", "arguments": {"params": {"engine": "apple_app_store", "term": "habit tracker"}}}
{"name": "search", "arguments": {"params": {"engine": "ebay", "_nkw": "vintage mechanical keyboard"}}}
Supported Engines: Google, Bing, Yahoo, DuckDuckGo, YouTube, eBay, and more (see serpapi://engines).
Result Types: Answer boxes, organic results, news, images, shopping - automatically detected and formatted.
Search responses preserve the existing MCP structuredContent.result string and include the same string in text content. For JSON output, result contains serialized JSON; existing clients can continue parsing it with JSON.parse(response.structuredContent.result). For Markdown output, it contains the unchanged Markdown. Errors and cancellations use the same wrapper. Search execution failures set isError: true; clients using FastMCP's high-level call_tool() should handle ToolError, or use call_tool_mcp() to inspect the result flag. See MCP tool results.
search uses the engine catalog and engine-specific rules to identify missing parameters. Supporting MCP 2026-07-28 clients receive a form before any search runs. Accepted answers are validated; decline or cancellation runs no search. Legacy clients and clients without form elicitation receive an error listing the missing parameters so the agent can ask in conversation. See MCP input requests.