AI agents answering financial questions need real-time data: stock news, earnings reports, market sentiment. An MCP server that wraps search provides this without building a custom data pipeline. This tutorial builds a financial news MCP tool that searches Google News for stock-specific information and Reddit for market sentiment, all through the Scavio API at $0.005 per search. Your agent gets grounded financial answers instead of hallucinated numbers.
Prerequisites
- Python 3.9+ installed
- requests library installed
- A Scavio API key from scavio.dev
- Basic understanding of MCP tool definitions
Walkthrough
Step 1: Build the financial news search functions
Create specialized search functions for stock news, earnings data, and market sentiment. Each function targets specific query patterns that return financial data.
Python
import os, requests, re, time
from datetime import datetime
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
URL = 'https://api.scavio.dev/api/v1/search'
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def search_stock_news(ticker: str, num: int = 5) -> list:
"""Get latest news for a stock ticker."""
resp = requests.post(URL, headers=H,
json={'query': f'{ticker} stock news 2026', 'country_code': 'us', 'num_results': num})
return [{'title': r['title'], 'snippet': r.get('snippet', ''),
'url': r['link'], 'source': 'google_news'}
for r in resp.json().get('organic_results', [])]
def search_earnings(ticker: str) -> list:
"""Search for recent earnings reports."""
resp = requests.post(URL, headers=H,
json={'query': f'{ticker} earnings report Q2 2026',
'country_code': 'us', 'num_results': 5})
return [{'title': r['title'], 'snippet': r.get('snippet', ''),
'url': r['link'], 'source': 'earnings'}
for r in resp.json().get('organic_results', [])]
def search_market_sentiment(ticker: str) -> list:
"""Check Reddit for market sentiment."""
resp = requests.post(URL, headers=H,
json={'query': f'site:reddit.com {ticker} stock',
'country_code': 'us', 'num_results': 5})
return [{'title': r['title'], 'snippet': r.get('snippet', ''),
'url': r['link'], 'source': 'reddit'}
for r in resp.json().get('organic_results', [])]
news = search_stock_news('AAPL')
print(f'AAPL news: {len(news)} articles')
for n in news[:3]:
print(f' {n["title"][:60]}')Step 2: Build the comprehensive stock report
Combine news, earnings, and sentiment into a unified financial report. Extract key signals like price mentions, analyst ratings, and sentiment indicators.
Python
def stock_report(ticker: str) -> dict:
"""Generate a comprehensive stock report."""
news = search_stock_news(ticker)
time.sleep(0.3)
earnings = search_earnings(ticker)
time.sleep(0.3)
sentiment = search_market_sentiment(ticker)
# Analyze sentiment from Reddit
all_reddit_text = ' '.join(r['snippet'] for r in sentiment).lower()
bullish_words = ['bull', 'buy', 'moon', 'undervalued', 'growth', 'strong']
bearish_words = ['bear', 'sell', 'overvalued', 'crash', 'decline', 'weak']
bull_count = sum(1 for w in bullish_words if w in all_reddit_text)
bear_count = sum(1 for w in bearish_words if w in all_reddit_text)
sentiment_label = 'bullish' if bull_count > bear_count else 'bearish' if bear_count > bull_count else 'neutral'
# Extract price mentions
all_text = ' '.join(r['snippet'] for r in news + earnings)
prices = re.findall(r'\$([\d,]+\.?\d*)', all_text)
return {
'ticker': ticker,
'timestamp': datetime.now().isoformat(),
'news_count': len(news),
'top_headlines': [n['title'] for n in news[:3]],
'earnings_count': len(earnings),
'reddit_sentiment': sentiment_label,
'sentiment_detail': {'bullish': bull_count, 'bearish': bear_count},
'price_mentions': prices[:5],
'sources': news + earnings + sentiment,
'credits_used': 3,
'cost': 0.015,
}
report = stock_report('NVDA')
print(f"Stock Report: {report['ticker']}")
print(f"Headlines: {len(report['top_headlines'])}")
for h in report['top_headlines']:
print(f' - {h[:60]}')
print(f"Reddit sentiment: {report['reddit_sentiment']}")
print(f"Cost: ${report['cost']}")Step 3: Define MCP tool schemas
Create MCP-compatible tool definitions that an AI agent can call. Each tool returns formatted financial data the LLM can use to answer questions.
Python
MCP_TOOLS = {
'stock_news': {
'name': 'stock_news',
'description': 'Get the latest news articles for a stock ticker symbol. Returns headlines, snippets, and source URLs.',
'inputSchema': {
'type': 'object',
'properties': {
'ticker': {'type': 'string', 'description': 'Stock ticker symbol (e.g., AAPL, NVDA, TSLA)'},
'num_results': {'type': 'integer', 'description': 'Number of articles (1-10)', 'default': 5}
},
'required': ['ticker']
}
},
'stock_report': {
'name': 'stock_report',
'description': 'Generate a comprehensive stock report with news, earnings, and Reddit sentiment analysis.',
'inputSchema': {
'type': 'object',
'properties': {
'ticker': {'type': 'string', 'description': 'Stock ticker symbol'}
},
'required': ['ticker']
}
},
'market_sentiment': {
'name': 'market_sentiment',
'description': 'Check Reddit sentiment for a stock or market topic.',
'inputSchema': {
'type': 'object',
'properties': {
'ticker': {'type': 'string', 'description': 'Stock ticker or topic'}
},
'required': ['ticker']
}
}
}
def handle_mcp_call(tool_name: str, args: dict) -> str:
if tool_name == 'stock_news':
news = search_stock_news(args['ticker'], args.get('num_results', 5))
return '\n\n'.join([f'{n["title"]}\n{n["snippet"]}\nSource: {n["url"]}' for n in news])
elif tool_name == 'stock_report':
report = stock_report(args['ticker'])
lines = [f'Stock Report: {report["ticker"]}',
f'Sentiment: {report["reddit_sentiment"]}',
f'Headlines:']
lines.extend(f' - {h}' for h in report['top_headlines'])
return '\n'.join(lines)
elif tool_name == 'market_sentiment':
results = search_market_sentiment(args['ticker'])
return '\n\n'.join(f'{r["title"]}\n{r["snippet"]}' for r in results)
return 'Unknown tool'
print('MCP tools defined:', list(MCP_TOOLS.keys()))
result = handle_mcp_call('stock_news', {'ticker': 'AAPL', 'num_results': 3})
print(result[:300])Step 4: Create the MCP server configuration
Set up the .mcp.json configuration so AI agents can connect to your financial news server.
Python
import json
def create_finance_mcp_config(output_path: str = '.mcp.json'):
"""Create MCP config with Scavio for financial data."""
config = {
'mcpServers': {
'scavio': {
'url': 'https://mcp.scavio.dev/mcp',
'headers': {
'Authorization': 'Bearer ${SCAVIO_API_KEY}'
}
}
}
}
with open(output_path, 'w') as f:
json.dump(config, f, indent=2)
print(f'MCP config written to {output_path}')
print(f'Financial tools available via Scavio MCP:')
print(f' - Web search (stock news, earnings, filings)')
print(f' - Reddit search (market sentiment)')
print(f' - YouTube search (analyst videos)')
print(f' Cost: $0.005 per search')
print(f' Endpoint: mcp.scavio.dev/mcp')
# Test the full pipeline
create_finance_mcp_config()
print('\nExample usage with Claude:')
print(' User: "What is the latest news on NVDA stock?"')
print(' Agent calls: stock_news({ticker: "NVDA"})')
print(' Agent gets: real-time news headlines + Reddit sentiment')
print(' Cost: $0.005-$0.015 per question')Python Example
Python
import os, requests, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def stock_news(ticker, num=5):
resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'query': f'{ticker} stock news 2026', 'country_code': 'us', 'num_results': num})
return resp.json().get('organic_results', [])
def stock_sentiment(ticker):
resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'query': f'site:reddit.com {ticker} stock', 'country_code': 'us', 'num_results': 5})
text = ' '.join(r.get('snippet','') for r in resp.json().get('organic_results', [])).lower()
bull = sum(1 for w in ['bull','buy','growth'] if w in text)
bear = sum(1 for w in ['bear','sell','crash'] if w in text)
return 'bullish' if bull > bear else 'bearish' if bear > bull else 'neutral'
for ticker in ['AAPL', 'NVDA']:
news = stock_news(ticker, 3)
print(f'{ticker}: {len(news)} articles, sentiment={stock_sentiment(ticker)}')
for n in news[:2]:
print(f' {n["title"][:60]}')
time.sleep(0.3)JavaScript Example
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function stockNews(ticker, num = 5) {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query: `${ticker} stock news 2026`, country_code: 'us', num_results: num })
});
return (await resp.json()).organic_results || [];
}
async function stockSentiment(ticker) {
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: `site:reddit.com ${ticker} stock`, country_code: 'us', num_results: 5 })
});
const text = ((await resp.json()).organic_results || []).map(r => r.snippet || '').join(' ').toLowerCase();
const bull = ['bull','buy','growth'].filter(w => text.includes(w)).length;
const bear = ['bear','sell','crash'].filter(w => text.includes(w)).length;
return bull > bear ? 'bullish' : bear > bull ? 'bearish' : 'neutral';
}
(async () => {
const news = await stockNews('AAPL', 3);
const sent = await stockSentiment('AAPL');
console.log(`AAPL: ${news.length} articles, sentiment=${sent}`);
news.slice(0, 2).forEach(n => console.log(` ${n.title.slice(0, 60)}`));
})();Expected Output
JSON
AAPL news: 5 articles
Apple Reports Record Q2 2026 Revenue Driven by AI
AAPL Stock Surges on Strong iPhone 17 Pre-orders
Apple Vision Pro 2 Launch Boosts Stock Price
Stock Report: NVDA
Headlines: 3
- NVIDIA H200 Demand Outstrips Supply in Q2 2026
- NVDA Earnings Beat Expectations by 15%
- NVIDIA Partners with AWS on Next-Gen AI Chips
Reddit sentiment: bullish
Cost: $0.015
MCP tools defined: ['stock_news', 'stock_report', 'market_sentiment']