MCP Search Server Ecosystem 2026

Model Context Protocol (MCP) has become the standard way AI agents discover and call tools. For web search, there are four practical MCP options in 2026: Scavio MCP (multi-platform, structured JSON), Tavily MCP (AI-summarized results), SearXNG MCP (self-hosted, free), and custom MCP servers wrapping any search API. Here is how each works, what it costs, and when to use it.

Scavio MCP: multi-platform structured search

Hosted at mcp.scavio.dev/mcp. Returns structured JSON from Google, Amazon, YouTube, Reddit, Walmart, and TikTok. Works with Claude Code, Cursor, VS Code, Windsurf, and ChatGPT.

{
  "mcpServers": {
    "scavio": {
      "url": "https://mcp.scavio.dev/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_SCAVIO_API_KEY"
      }
    }
  }
}

Cost: 50 free credits on signup, then $0.01/credit. Each search = 1 credit. Best for agents that need structured data from multiple platforms.

Tavily MCP: AI-summarized search

{
  "mcpServers": {
    "tavily": {
      "command": "npx",
      "args": ["-y", "tavily-mcp"],
      "env": {
        "TAVILY_API_KEY": "YOUR_TAVILY_API_KEY"
      }
    }
  }
}

Cost: 1K free searches/mo, then $0.008/credit. Returns AI-generated summaries rather than raw search results. Best for agents that need context for reasoning, not raw data for extraction.

SearXNG MCP: self-hosted, free

# Deploy SearXNG
docker run -d --name searxng -p 8080:8080 searxng/searxng:latest

# Use community MCP server
# github.com/ihor-sokoliuk/mcp-searxng
npx mcp-searxng --searxng-url http://localhost:8080

Cost: $5-10/mo for hosting. Unlimited searches. Results are less structured and sometimes inconsistent. Best for budget-constrained setups where you control infrastructure.

Custom MCP server wrapping any API

from mcp.server.fastmcp import FastMCP
import requests, os

mcp = FastMCP("custom-search")
API = "https://api.scavio.dev"
H = {"Authorization": f"Bearer {os.environ['SCAVIO_API_KEY']}"}

@mcp.tool()
def web_search(query: str) -> str:
    """Search Google via the Scavio API; returns the top 5 organic results."""
    resp = requests.post(f"{API}/api/v2/google", headers=H,
        json={"query": query}, timeout=90)
    results = resp.json().get("organic_results", [])[:5]
    return "\n".join(f"- {r.get('title')}: {r.get('link')}" for r in results)

@mcp.tool()
def reddit_search(query: str) -> str:
    """Search Reddit posts via the Scavio API; returns the top 5."""
    resp = requests.post(f"{API}/api/v1/reddit/search", headers=H,
        json={"query": query}, timeout=90)
    posts = resp.json()["data"].get("results", [])[:5]
    return "\n".join(f"- r/{p.get('subreddit')}: {p.get('title')} {p.get('url')}" for p in posts)

if __name__ == "__main__":
    mcp.run()  # stdio transport

Comparison table

• Scavio MCP: hosted, multi-platform, structured JSON, 50 free on signup, $0.01/query after on pay-as-you-go
• Tavily MCP: local process, AI summaries, 1K free/mo, $0.008/query after
• SearXNG MCP: self-hosted, meta-search, unlimited, $5-10/mo hosting
• Custom MCP: any backend, full control, development effort required

Which MCP server for which agent

Claude Code / Cursor / Windsurf users: Scavio MCP (hosted, no setup). LangChain / CrewAI agents: HTTP API is simpler than MCP for framework integrations. Budget projects: SearXNG MCP. Need to wrap multiple data sources: custom MCP server. The MCP layer is a transport choice, not a quality differentiator. What matters is the underlying search data.