The Problem
Coding agents waste time guessing at class names, SDK methods, and API patterns from stale training data. A pre-coding search routine eliminates this.
How Scavio Helps
- 60-second routine saves hours of debugging
- Verify SDK versions before writing code
- Load codebase context via memory MCP
- Search documentation via web MCP
- Prevent entire categories of hallucination
Relevant Platforms
Web search with knowledge graph, PAA, and AI overviews
Quick Start: Python Example
Here is a quick example searching Google for "next.js 15 app router api routes":
Python
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 Developers using Claude Code, Cursor, or other AI coding tools
Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your mcp pre-coding search routine 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.