SEO & Web Analysis MCP Server
Lighthouse 감사, PageSpeed 분석, SSL 확인, 기술 스택 탐지, WHOIS 및 DNS 쿼리
문서
SEO & Web Analysis MCP — Crawl, Tech & DNS for Agents (nexgendata/seo-web-analysis-mcp-server) Actor
MCP server exposing on-page crawl, tech-stack detection, DNS, SSL and WHOIS as agent tools. Connect Claude, Cursor, n8n or the OpenAI Agents SDK.
- URL: https://apify.com/nexgendata/seo-web-analysis-mcp-server.md
- Developed by: NexGenData (community)
- Categories: AI, SEO tools, MCP servers
- Stats: 7 total users, 2 monthly users, 100.0% runs succeeded, 1 bookmarks
- User rating: No ratings yet
Pricing
from $10.00 / 1,000 results
This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A".
How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official JavaScript/TypeScript client:
npm install apify-client
In Python projects, use official Python client library:
pip install apify-client
In shell scripts, use Apify CLI:
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## SEO & Web Analysis MCP
A Model Context Protocol server that gives AI agents SEO and site-analysis tools — on-page crawl, tech-stack detection, DNS, SSL and WHOIS — as callable tools. For SEO, audit and recon agents.
### 🛠 Tools (5)
- `check_dns` — DNS records for a domain.
- `check_ssl` — SSL/TLS certificate details.
- `crawl_website` — Crawl a site for on-page SEO data.
- `detect_tech_stack` — Detect a site's technology stack.
- `lookup_whois` — WHOIS registration data for a domain.
### 🔌 Connect (Claude Desktop / Cursor / n8n / OpenAI Agents SDK)
Add this MCP server to your client config:
```json
{
"mcpServers": {
"seo-web-analysis": {
"url": "https://nexgendata--seo-web-analysis-mcp-server.apify.actor/mcp"
}
}
}
Sample agent prompt:
Crawl a site for on-page SEO issues and detect its technology stack.
Pricing: $0.02 per tool call (Pay-Per-Event). Runs in Standby mode.
🔍 SEO & Web Analysis MCP Server by NexGenData
Connect AI agents to website and domain analysis data through the Model Context Protocol (MCP). This server exposes a focused set of web-intelligence tools that LLM-powered applications like Claude, ChatGPT, Cursor, and Cline can call directly, returning clean JSON.
What This MCP Server Does
This MCP server exposes web and domain analysis tools that any MCP-compatible AI client can call. Instead of building API integrations or scraping code, your agent can crawl a website, detect its tech stack, and inspect its DNS, SSL, and WHOIS records through tool calls. The server handles extraction, rate limiting, and response formatting.
Available Tools
This MCP server exposes 5 tools:
crawl_website— crawl a website and return page content / structuredetect_tech_stack— identify technologies used by a sitecheck_dns— look up DNS records for a domaincheck_ssl— inspect a domain's SSL/TLS certificatelookup_whois— retrieve WHOIS registration data for a domain
Each tool accepts structured parameters and returns JSON that AI agents can parse and chain together for multi-step workflows.
Who Uses This
- AI developers building agents that need website and domain analysis
- Automation platforms connecting AI agents to structured web data
- Research and ops teams using AI assistants for site and domain checks
Getting Started
This actor runs in standby mode and exposes an MCP endpoint. Configure your MCP client (Claude Desktop, Cursor, Cline, or a custom agent) with the server endpoint URL and your Apify API token. The server auto-discovers the available tools.
FAQ
Is this compatible with Claude Desktop? Yes. Any MCP-compatible client can connect, including Claude Desktop, Cursor, Cline, and custom LLM agents.
Do I need separate API keys? No. You only need your Apify API token to connect.
Can I use this in production? Yes. The server runs on Apify's scalable infrastructure with built-in monitoring, logging, and error handling.
🔗 Related NexGenData MCP Servers
| Use case | Actor |
|---|---|
| Web-scraping MCP (any URL, AI agents) | web-scraping-mcp-server |
| Domain intelligence MCP (DNS / WHOIS / SSL) | domain-intelligence-mcp-server |
| Developer tools MCP | developer-tools-mcp-server |
| Reddit MCP (audience research) | reddit-mcp-server |
Browse the full NexGenData catalog at https://apify.com/nexgendata
Actor input Schema
Actor input object example
{}
API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
JavaScript example
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
token: '<YOUR_API_TOKEN>',
});
// Prepare Actor input
const input = {};
// Run the Actor and wait for it to finish
const run = await client.actor("nexgendata/seo-web-analysis-mcp-server").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
Python example
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")
# Prepare the Actor input
run_input = {}
# Run the Actor and wait for it to finish
run = client.actor("nexgendata/seo-web-analysis-mcp-server").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
CLI example
echo '{}' |
apify call nexgendata/seo-web-analysis-mcp-server --silent --output-dataset
MCP server setup
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=nexgendata/seo-web-analysis-mcp-server",
"--header",
"Authorization: Bearer <YOUR_API_TOKEN>"
]
}
}
}
OpenAPI specification
{
"openapi": "3.0.1",
"info": {
"title": "SEO & Web Analysis MCP — Crawl, Tech & DNS for Agents",
"description": "MCP server exposing on-page crawl, tech-stack detection, DNS, SSL and WHOIS as agent tools. Connect Claude, Cursor, n8n or the OpenAI Agents SDK.",
"version": "0.0",
"x-build-id": "J6bA3sUnkjmmU14PH"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/nexgendata~seo-web-analysis-mcp-server/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-nexgendata-seo-web-analysis-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/nexgendata~seo-web-analysis-mcp-server/runs": {
"post": {
"operationId": "runs-sync-nexgendata-seo-web-analysis-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/nexgendata~seo-web-analysis-mcp-server/run-sync": {
"post": {
"operationId": "run-sync-nexgendata-seo-web-analysis-mcp-server",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}