Zenrows Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Zenrows Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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The ZenRows MCP (Model Context Protocol) server is the standard way AI systems use ZenRows. A single connection gives your AI assistant, agent, or application real-time access to any website.
📚 Full documentation: docs.zenrows.com/integrations/mcp/mcp-overview
ZenRows MCP supports two transport options. Both expose the same set of tools and capabilities. Pick the one that fits your client.
Use the hosted ZenRows MCP server when your AI application calls an LLM API directly. The server runs on ZenRows infrastructure, so there is nothing to install, configure, or update.
Server URL:
https://mcp.zenrows.com/mcpTransport: Streamable HTTP
Authentication: OAuth-based. Pass your ZenRows API key as a Bearer token in the Authorization header on every request.
Authorization: Bearer YOUR_ZENROWS_API_KEYMost MCP clients accept this through an authorization shorthand field on the tool config and forward it as the Bearer token automatically. Some clients use a free-form headers field instead. Either approach works.
#### Example: OpenAI Responses API
import os
from openai import OpenAI
ZENROWS_API_KEY = os.environ["ZENROWS_API_KEY"]
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
response = client.responses.create(
model="gpt-5",
tools=[
{
"type": "mcp",
"server_label": "zenrows",
"server_description": "Web scraping MCP server for accessing live web content.",
"server_url": "https://mcp.zenrows.com/mcp",
"authorization": ZENROWS_API_KEY,
"require_approval": "never",
}
],
input="Visit https://news.ycombinator.com/ and summarize the three most recent posts.",
)
print(response.output_text)For the full walkthrough with framework-specific examples, see the Remote MCP server docs.
Use the local stdio configuration when your MCP client runs the server as a local subprocess instead of calling a remote URL. This is the standard setup for desktop AI tools and IDE plugins, including Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, Zed, and JetBrains IDEs.
Package: @zenrows/mcp on npm
Authentication: API key via the ZENROWS_API_KEY environment variable.
Requirements: Node.js installed (for npx to work).
Configuration:
{
"mcpServers": {
"zenrows": {
"command": "npx",
"args": ["-y", "@zenrows/mcp"],
"env": {
"ZENROWS_API_KEY": "YOUR_ZENROWS_API_KEY"
}
}
}
}The exact location of this config varies by client. See the per-client setup guides for the file path for your client.
The ZenRows MCP exposes two families of tools:
The AI selects the right tool from your prompt. You don't call tools directly in code.
See the full tool reference for every tool, parameter, and return value.
git clone https://github.com/ZenRows/zenrows-mcp
cd zenrows-mcp
npm install
cp .env.example .env # Add your API key
npm run dev # Run with .env loaded (requires Node.js 20.6+)
npm run build # Compile to dist/
npm run inspect # Open the MCP inspector UIPull requests and issues are welcome.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.