One API, Five Platforms: Search for Agent Developers

Agent developers need search across Google, Amazon, YouTube, Reddit, and Walmart through one API. Maintaining five separate integrations with five auth systems, five response formats, and five rate limit policies is the kind of plumbing work that slows agent development from days to weeks.

The Five-Platform Problem

A product research agent needs Google for web results, Amazon for pricing, YouTube for reviews, Reddit for community opinions, and Walmart for price comparison. Each platform has its own API (if it exists), its own auth mechanism, its own response format, and its own rate limits. Building adapters for all five takes a week of integration work before you write any agent logic.

One API, Five Platforms

Scavio wraps all five platforms behind a single POST endpoint. Same auth header, same request format, same response structure. Switch platforms by changing one field.

import requests, os

H = {"x-api-key": os.environ["SCAVIO_API_KEY"]}
URL = "https://api.scavio.dev/api/v1/search"

def search(query, platform="google"):
    """One function, five platforms."""
    r = requests.post(URL, headers=H,
        json={"platform": platform, "query": query},
        timeout=10
    ).json()
    return r.get("organic", [])

# Product research in 5 lines
product = "sony wh-1000xm5"
web = search(f"{product} review 2026", "google")
prices = search(product, "amazon")
videos = search(f"{product} review", "youtube")
opinions = search(product, "reddit")
walmart = search(product, "walmart")

print(f"Web: {len(web)}, Amazon: {len(prices)}, "
      f"YouTube: {len(videos)}, Reddit: {len(opinions)}, "
      f"Walmart: {len(walmart)}")

Agent Tool Definition

For LangChain, CrewAI, or custom agents, define one tool that accepts a platform parameter. The agent chooses which platform to search based on the task context.

from langchain.tools import Tool

def multi_platform_search(query_with_platform: str) -> str:
    """Parse 'platform:query' format and search."""
    parts = query_with_platform.split(":", 1)
    if len(parts) == 2 and parts[0] in ["google","amazon","youtube","reddit","walmart"]:
        platform, query = parts
    else:
        platform, query = "google", query_with_platform

    results = search(query, platform)
    return "\n".join([
        f"- {r.get('title', '')}: {r.get('snippet', '')[:100]}"
        for r in results[:5]
    ])

search_tool = Tool(
    name="multi_search",
    description=(
        "Search across platforms. Format: 'platform:query'. "
        "Platforms: google, amazon, youtube, reddit, walmart. "
        "Example: 'amazon:wireless earbuds'"
    ),
    func=multi_platform_search,
)

# Agent uses it naturally:
# "amazon:wireless earbuds under $50"
# "reddit:best headphones for commuting"
# "youtube:noise cancelling comparison 2026"

MCP Integration

For MCP-compatible clients (Claude, Cursor), the Scavio MCP server at https://mcp.scavio.dev/mcp exposes all five platforms as tools. No custom tool definitions needed -- the MCP server advertises its capabilities and the client discovers them automatically.

{
  "mcpServers": {
    "search": {
      "url": "https://mcp.scavio.dev/mcp",
      "headers": {
        "x-api-key": "your-api-key"
      }
    }
  }
}

Practical Agent Workflows

Product research agent: Google for reviews, Amazon for pricing, YouTube for video reviews, Reddit for honest opinions, Walmart for price comparison. Competitive intelligence agent: Google for competitor news, Reddit for user complaints, YouTube for competitor demos. Content agent: Google for trending topics, YouTube for content gaps, Reddit for audience questions.

Cost at Agent Scale

A product research query that hits all 5 platforms = 5 credits = $0.025. Running 100 product research tasks/day = $2.50/day = $75/month. Scavio pricing: $0.005/credit, 250 free/month, $30/month for 7K credits. At agent scale, the multi-platform approach costs a fraction of maintaining individual API subscriptions.