Mls Scraper

Extract sports scores, stats, and team information from Mls. Built on spider-browser .

Get started Docs

target
mls.com: success rate
99.9%: latency
~4ms

Quick start

Extract data in minutes.

mls-com-scraper.ts

import { Spider } from "@spider-cloud/spider-client";

const spider = new Spider({ apiKey: process.env.SPIDER_API_KEY! });

const result = await spider.scrapeUrl("https://www.mls.com", {
  return_format: "markdown",
});

console.log(result);

ready to run · spider-browser · TypeScript

Fetch API

One endpoint for mls.com.

Structured JSON from mls.com with a single POST. AI-resolved selectors, cached on the first call.

POST /fetch/mls.com/

TeamScorePlayerDateLeagueStats

Try it Fetch docs

cURL

curl -X POST https://api.spider.cloud/fetch/mls.com/ \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"return_format": "json"}'

Python

import requests

resp = requests.post(
    "https://api.spider.cloud/fetch/mls.com/",
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json",
    },
    json={"return_format": "json"},
)
print(resp.json())

Node.js

const resp = await fetch("https://api.spider.cloud/fetch/mls.com/", {
  method: "POST",
  headers: {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({ return_format: "json" }),
});
const data = await resp.json();
console.log(data);

Extraction