Choosing Search APIs for Claude Code: Real Team Picks

The search APIs that work best with Claude Code in 2026 are Scavio (for structured SERP data via MCP), Tavily (for RAG-ready content), and Context7 (for documentation lookup). The deciding factor is token efficiency: how much useful signal you get per token returned, because Claude Code pays for every token in the context window.

Why token efficiency matters for Claude Code

A thread on r/ClaudeCode asked "Best Search APIs for Claude Code in 2026?" and the top-voted answers all mentioned the same problem: most search APIs return too much noise. When Claude Code calls a search tool via MCP, the full response goes into the context window. A bloated response with ads, related searches, and HTML fragments wastes tokens and degrades the agent's reasoning about the actual results.

The ideal search API for Claude Code returns structured, clean data with no filler. That means JSON with titles, URLs, snippets, and metadata — not raw HTML or paginated web results.

Scavio MCP

Scavio exposes an MCP endpoint at https://mcp.scavio.dev/mcp. In your Claude Code MCP config, you add it as a server and Claude can call it directly during a session. The response is structured JSON: organic results, AI Overviews, People Also Ask, and platform-specific data for Google, Amazon, YouTube, Walmart, and Reddit.

Pricing: 500 free credits/month, $30/month for 7,000 credits. Each search is one credit regardless of platform.

// .mcp.json configuration
{
  "mcpServers": {
    "scavio": {
      "type": "url",
      "url": "https://mcp.scavio.dev/mcp",
      "headers": {
        "x-api-key": "YOUR_KEY"
      }
    }
  }
}

Once configured, Claude Code can call Scavio search inline. For example, during a coding session you can say "search Google for the latest Next.js 15 deployment docs" and Claude will call the MCP tool, get structured results, and use them in context.

Tavily

Tavily's API returns AI-optimized search results: pre-extracted content, source attribution, and relevance scoring. 1,000 free queries/month, $30/month paid. After the Nebius acquisition in February 2026, the API has been stable but the long-term roadmap is unclear. Tavily has a community MCP server that works with Claude Code.

The advantage for Claude Code: Tavily pre-processes content so the response is already LLM-ready. The disadvantage: you get Tavily's interpretation of the content, not the raw SERP structure. If you need to know ranking positions, featured snippets, or AI Overview data, Tavily does not provide that.

Linkup

Linkup offers a deep search mode that fetches and parses full page content, not just snippets. Pricing is around EUR 50 per 1,000 deep queries, with EUR 5 free per month. The deep mode is useful when Claude Code needs to understand the full content of a page, not just the title and snippet. The trade-off is cost and latency — deep queries take longer and cost 10x more than standard search.

Brave Search API

$5 per 1,000 requests, $5 free credit for new accounts. Requires "Powered by Brave Search" attribution. The response structure is clean but Brave's index is smaller than Google's, which matters for niche technical queries. Several Claude Code users on r/ClaudeCode reported inconsistent results for programming-related searches compared to Google-based APIs.

Context7

Context7 is a documentation-specific MCP server. It indexes popular library docs and returns relevant sections. Not a general web search API — it is purpose-built for "look up the docs for this library" queries. Combines well with a general search API: use Context7 for docs, Scavio for everything else.

What teams actually pick

Based on r/ClaudeCode discussions and the setups shared by teams using Claude Code in production, the most common pattern is:

• Scavio MCP for general web search, SERP data, and multi-platform queries (Google + Amazon + YouTube)
• Context7 for library documentation lookup
• Tavily as an alternative if the team is already in the Tavily ecosystem or needs RAG-optimized responses

Testing token efficiency yourself

Before committing to an API, run the same 10 queries across each provider and count tokens in the response. Here is a quick way to do it with Scavio:

import requests
import tiktoken

enc = tiktoken.encoding_for_model("cl100k_base")

queries = [
    "next.js 15 server actions",
    "python asyncio best practices 2026",
    "react server components vs client components"
]

for q in queries:
    resp = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": "YOUR_KEY"},
        json={"platform": "google", "query": q, "num_results": 5}
    )
    text = resp.text
    tokens = len(enc.encode(text))
    print(f"Query: {q}")
    print(f"  Response tokens: {tokens}")
    print(f"  Results returned: {len(resp.json().get('organic_results', []))}")
    print()

Bottom line

For Claude Code, the best search API is the one that returns the most signal per token. Scavio's structured SERP JSON and native MCP endpoint make it the default pick for teams that need general web search. Context7 fills the documentation gap. Tavily works if you want pre-digested content for RAG. Avoid APIs that return HTML or unstructured text — you are paying for those tokens twice: once to the search provider and once to the LLM.