MCP (Model Context Protocol) lets AI models call external tools through a standardized interface. The Scavio MCP server at mcp.scavio.dev/mcp exposes web search, TikTok analytics, and YouTube data as MCP tools that any compatible client can call. This tutorial connects the Scavio MCP server to local LLMs running on Ollama or llama.cpp, giving your local models the same search capabilities as cloud-hosted agents. Setup takes under 5 minutes and each tool call costs $0.005.
Prerequisites
- Ollama installed and running locally
- Python 3.9+ installed
- A Scavio API key from scavio.dev
- Basic understanding of MCP tool protocol
Walkthrough
Step 1: Create the MCP configuration file
Write a .mcp.json file that points to the Scavio MCP server. This file tells MCP-compatible clients where to find the search tools.
Python
import json
mcp_config = {
'mcpServers': {
'scavio': {
'url': 'https://mcp.scavio.dev/mcp',
'headers': {
'Authorization': 'Bearer ${SCAVIO_API_KEY}'
}
}
}
}
with open('.mcp.json', 'w') as f:
json.dump(mcp_config, f, indent=2)
print('MCP config written to .mcp.json')
print('Available tools: web_search, tiktok_search, youtube_search')Step 2: Test the MCP connection with a direct call
Before wiring to Ollama, test that the MCP server responds correctly by making a direct HTTP request to the search tool.
Python
import os, requests
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
# Direct test of the search endpoint
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
json={'query': 'test search query', 'country_code': 'us', 'num_results': 3})
data = resp.json()
print(f'Status: {resp.status_code}')
print(f'Results: {len(data.get("organic_results", []))}')
for r in data.get('organic_results', [])[:3]:
print(f' {r["title"]}')Step 3: Wire MCP tools to Ollama
Create an Ollama tool wrapper that translates MCP tool calls into Scavio API requests. This bridges the gap between Ollama's tool format and MCP.
Python
import ollama
def handle_mcp_tool(tool_name: str, args: dict) -> str:
"""Route MCP tool calls to the Scavio API."""
if tool_name == 'web_search':
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
json={'query': args.get('query', ''), 'country_code': 'us', 'num_results': 5})
results = resp.json().get('organic_results', [])
return '\n'.join(f'{r["title"]}: {r.get("snippet", "")}' for r in results)
elif tool_name == 'tiktok_search':
resp = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos',
headers={'Authorization': f'Bearer {SCAVIO_KEY}', 'Content-Type': 'application/json'},
json={'keyword': args.get('query', ''), 'count': 5, 'cursor': 0})
videos = resp.json().get('data', {}).get('videos', [])
return '\n'.join(f'{v.get("desc", "")[:80]} ({v.get("stats", {}).get("playCount", 0)} plays)' for v in videos)
return 'Unknown tool'
tools = [
{'type': 'function', 'function': {'name': 'web_search', 'description': 'Search the web for current information',
'parameters': {'type': 'object', 'properties': {'query': {'type': 'string'}}, 'required': ['query']}}},
{'type': 'function', 'function': {'name': 'tiktok_search', 'description': 'Search TikTok videos',
'parameters': {'type': 'object', 'properties': {'query': {'type': 'string'}}, 'required': ['query']}}},
]
response = ollama.chat(model='llama3.1', messages=[{'role': 'user', 'content': 'What is trending on TikTok right now?'}], tools=tools)
if response.message.tool_calls:
for tc in response.message.tool_calls:
result = handle_mcp_tool(tc.function.name, tc.function.arguments)
print(f'Tool: {tc.function.name}')
print(result[:300])Python Example
Python
import os, requests, json
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
# Create MCP config
config = {'mcpServers': {'scavio': {
'url': 'https://mcp.scavio.dev/mcp',
'headers': {'Authorization': f'Bearer {SCAVIO_KEY}'}}}}
with open('.mcp.json', 'w') as f:
json.dump(config, f, indent=2)
# Test search via MCP
def mcp_search(query):
resp = requests.post('https://api.scavio.dev/api/v1/search',
headers={'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'},
json={'query': query, 'country_code': 'us', 'num_results': 5})
return resp.json().get('organic_results', [])
results = mcp_search('latest AI news')
print(f'MCP search returned {len(results)} results')
for r in results[:3]:
print(f' {r["title"]}')JavaScript Example
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
const fs = require('fs');
// Create MCP config
const config = { mcpServers: { scavio: {
url: 'https://mcp.scavio.dev/mcp',
headers: { Authorization: `Bearer ${SCAVIO_KEY}` }
}}};
fs.writeFileSync('.mcp.json', JSON.stringify(config, null, 2));
// Test search
async function mcpSearch(query) {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query, country_code: 'us', num_results: 5 })
});
const data = await resp.json();
return data.organic_results || [];
}
mcpSearch('latest AI news').then(r => {
console.log(`MCP search: ${r.length} results`);
r.slice(0, 3).forEach(x => console.log(` ${x.title}`));
});Expected Output
JSON
MCP config written to .mcp.json
Available tools: web_search, tiktok_search, youtube_search
Status: 200
Results: 3
Latest AI News and Developments 2026
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Tool: tiktok_search
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