An r/LocalLLM user wanted local LLM for privacy but could not get good search results — even Tavily did not yield results. This tutorial sets up Scavio MCP with Ollama for reliable search grounding.
Prerequisites
- Ollama installed
- Scavio API key
- Agent runtime (opencode, Claude Code, or custom)
Walkthrough
Step 1: Install and run Ollama with a capable model
Pull a model that supports tool calling.
Bash
ollama pull qwen3.6:27b
# Or: ollama pull llama3.3:latest
# Model must support function/tool calling for agentic searchStep 2: Configure Scavio MCP
Add Scavio as an MCP server.
Bash
# For opencode/Claude Code:
claude mcp add scavio https://mcp.scavio.dev/mcp --header 'x-api-key: YOUR_SCAVIO_KEY'
# For custom setups, configure in mcp.json:
# { "mcpServers": { "scavio": { "url": "https://mcp.scavio.dev/mcp", "headers": { "x-api-key": "KEY" } } } }Step 3: Set up the agent loop
Model decides when to search vs answer from knowledge.
Python
import ollama, requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def search(query):
return requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'platform': 'google', 'query': query}).json()
def agent(question):
response = ollama.chat(model='qwen3.6:27b', messages=[
{'role': 'system', 'content': 'Search for factual questions. Answer reasoning questions directly.'},
{'role': 'user', 'content': question}])
# Handle tool calls if model requests search
return responseStep 4: Test with factual vs reasoning queries
Verify search grounding works correctly.
Text
# Factual (should search): 'What is the current price of Helium 10?'
# Reasoning (should not search): 'Explain gradient descent in simple terms'
# Mixed: 'Compare current LLM pricing across providers'Python Example
Python
# Privacy model: your query goes to Scavio (zero data retention)
# but NOT to OpenAI/Anthropic. The LLM runs locally.
# Search API is the only external call.JavaScript Example
JavaScript
// Same pattern with Ollama JS client.Expected Output
JSON
Local Ollama model grounded with live search. Factual queries get accurate answers; reasoning queries use local knowledge. Privacy preserved.