Scavio for ML Dataset Discovery via MCP Pipeline

The Problem

ML engineers spend hours searching for training data across scattered repositories. No single source covers all available datasets, and manual searching misses newly published datasets.

How Scavio Helps

• Search multiple dataset sources via MCP servers in one agent session
• Google search for dataset announcements and repositories
• Cross-reference with Hugging Face, Kaggle, and academic databases
• Automated dataset cataloging with metadata extraction
• Cost: $0.005/query for web search layer of the discovery pipeline

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "healthcare sentiment analysis dataset 2026":

import requests

API_KEY = "your_scavio_api_key"

response = requests.post(
    "https://api.scavio.dev/api/v1/search",
    headers={
        "x-api-key": API_KEY,
        "Content-Type": "application/json",
    },
    json={"query": query},
)

data = response.json()
for result in data.get("organic_results", [])[:5]:
    print(f"{result['position']}. {result['title']}")
    print(f"   {result['link']}\n")

Built for ML engineers, data scientists, and research teams building training and evaluation datasets

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your ml dataset discovery via mcp pipeline solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (50 credits on signup, no credit card required) and scale to paid plans when you need higher volume.