Tavily vs Searxng MCP: Which Web Search Server is Better for OpenCode?

Mar 26, 2026

I was trying to get OpenCode to search the web for the latest documentation on a React library. It kept giving me outdated information from its training data. That’s when I realized: OpenCode has no built-in web search capability.

By default, OpenCode can only access information from its training data. It’s essentially blind to the current internet. No researching current topics, no finding up-to-date documentation, no verifying facts from live sources.

The solution? MCP (Model Context Protocol) web search servers. But which one should I use? I tested both Tavily and Searxng to find out.

When I first started using OpenCode, I asked it to research a library that had a major update last month. The response was completely wrong because it was working from stale training data.

OpenCode needs MCP servers for web capabilities. Without a web search server, you can’t:

Research current events or recent library changes
Find up-to-date documentation
Verify facts from live sources
Gather real-time information

This is arguably the most essential MCP capability for OpenCode. Let me show you the two main options.

Option 1: Tavily MCP (The Easy Path)

Tavily is an API-based web search service optimized for AI agents. It took me about 5 minutes to set up.

Setup:

First, get an API key from tavily.com. Then configure OpenCode:

{
  "mcpServers": {
    "tavily": {
      "command": "npx",
      "args": ["-y", "@tavily/mcp-server"],
      "env": {
        "TAVILY_API_KEY": "${TAVILY_API_KEY}"
      }
    }
  }
}

That’s it. No Docker, no servers to maintain.

What you get:

Tavily provides two tools: tavily_search and tavily_extract.

const results = await tavily.search({
  query: "OpenCode MCP server configuration 2025",
  maxResults: 5,
  includeAnswer: true
})

// results.answer: AI-generated summary
// results.results: [{url, title, content, score}, ...]

const extracted = await tavily.extract({
  urls: [
    "https://docs.opencode.ai/mcp",
    "https://github.com/opencode-ai/opencode"
  ],
  extractDepth: "advanced"
})

The good:

5-minute setup with just an API key
Free tier: 1,000 searches/month
Clean, structured JSON output optimized for AI
Built-in web scraping with tavily_extract
No infrastructure to maintain

The bad:

API rate limits (depends on your plan)
Data goes through a third-party service
Free tier runs out fast with heavy use

I hit the rate limit on day two of a research-heavy project. That’s when I started looking at Searxng.

Option 2: Searxng MCP (The Self-Hosted Path)

Searxng is a self-hosted metasearch engine that aggregates results from 70+ search engines. It’s privacy-focused with no tracking.

Setup is two parts: deploy Searxng, then configure the MCP server.

Part 1: Deploy Searxng with Docker:

docker run -d -p 8888:8080 searxng/searxng

Or with docker-compose for more control:

version: '3'
services:
  searxng:
    image: searxng/searxng:latest
    ports:
      - "8888:8080"
    environment:
      - SEARXNG_BASE_URL=http://localhost:8888/
    volumes:
      - ./searxng:/etc/searxng

Part 2: Configure the MCP server:

{
  "mcpServers": {
    "searxng": {
      "command": "npx",
      "args": ["-y", "searxng-mcp-server"],
      "env": {
        "SEARXNG_URL": "http://localhost:8888"
      }
    }
  }
}

Usage:

const results = await searxng.search({
  query: "OpenCode MCP server configuration",
  pageno: 1
})

// Returns aggregated results from multiple engines
results.forEach(result => {
  console.log(result.title, result.url, result.engine)
})

The good:

No API keys or rate limits
Complete privacy (self-hosted)
Free unlimited searches
Aggregates 70+ search engines
Works offline after initial setup

The bad:

Requires Docker/server setup
More complex configuration
Need to maintain infrastructure
No built-in content extraction
Setup took me about 45 minutes

Decision Matrix

I put together a comparison table to help decide:

| Factor              | Tavily                    | Searxng                        |
|---------------------|---------------------------|--------------------------------|
| Setup Time          | 5 minutes                 | 30-60 minutes                  |
| Cost                | Free tier + paid plans    | Free (self-hosted)             |
| API Required        | Yes                       | No                             |
| Rate Limits         | Yes (per plan)            | No                             |
| Privacy             | Third-party service       | Self-hosted                    |
| Maintenance         | None                      | Docker/server                  |
| Content Extraction  | Built-in                  | Requires separate tool         |
| Search Quality      | AI-optimized              | Aggregated from 70+ engines     |
| Best For            | Quick start, structured   | Privacy, unlimited use         |

Common Mistakes I Made

Mistake 1: Choosing based only on ease of setup

I started with Tavily because it was easy. But I didn’t consider long-term costs and privacy needs. For a research-heavy project, I should have started with Searxng.

Mistake 2: Ignoring rate limits

Tavily’s free tier (1,000/month) ran out fast when I was doing extensive research on multiple topics. I had to either upgrade or switch.

Mistake 3: Not testing both options first

Search quality and result formats differ between services. I should have tested with my actual use cases before committing to one.

Mistake 4: Forgetting about content extraction

Tavily has built-in extract tool for scraping content from URLs. Searxng doesn’t, so I needed a separate solution for that.

Mistake 5: Overlooking multi-agent setups

Later I learned that Searxng works well with DCP and multi-agent orchestrators. Tavily is more suited for single-agent workflows.

When to Choose What

Choose Tavily if:

You want to get started in 5 minutes
You need structured, AI-optimized results
You’re okay with API rate limits
You want built-in content extraction
You don’t want to manage infrastructure

Choose Searxng if:

Privacy is a top priority
You expect heavy search usage
You’re comfortable with Docker
You want unlimited free searches
You’re building multi-agent systems

The hybrid approach:

Some users run both. Tavily for quick searches and structured extraction, Searxng as a fallback when hitting Tavily rate limits. This gives you the best of both worlds if you’re willing to maintain the extra complexity.

What I Ended Up Using

For my daily work with OpenCode, I use Tavily. The 5-minute setup and clean API won me over. When I hit rate limits, I either upgrade my plan or temporarily switch to Searxng.

For my privacy-sensitive projects, I use Searxng exclusively. The unlimited searches and no third-party data sharing are worth the infrastructure overhead.

The right choice depends on your priorities. Both are solid options that solve the core problem: giving OpenCode the ability to search the web.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!