Financial Data MCP Beyond Tavily in 2026
Stock news MCP servers typically use Tavily. Broader search API adds SEC filings, earnings transcripts, and competitor data in one MCP.
Most financial data MCP servers use Tavily as their search backend. This works for basic stock news but misses SEC filings, competitor analysis, earnings call transcripts, and analyst coverage that a broader search API captures. Replacing the Tavily layer with a multi-source search API gives your financial MCP server deeper coverage without adding complexity.
The standard Tavily financial MCP
The typical setup: a Claude Desktop or agent framework connects to an MCP server that wraps Tavily search for financial queries. When you ask "what happened to NVDA today," it searches Tavily, gets 5 news results, and feeds them as context. This covers breaking news well. It misses everything else: SEC 10-K filings, institutional ownership changes, supply chain analysis, competitor financial data, and regulatory actions.
Tavily pricing: 1K free searches/month, $30/month for 10K searches (Researcher tier). Since Nebius acquired Tavily in February 2026, the product direction has shifted toward enterprise RAG features. The search quality for financial data has been inconsistent, particularly for queries that need results beyond recent headlines.
Broader search API approach
import requests, os, json
SCAVIO_KEY = os.environ["SCAVIO_API_KEY"]
def financial_search(query, count=10):
"""Search for financial data across all indexed sources."""
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": SCAVIO_KEY},
json={"query": query, "num_results": count}
)
return resp.json()["results"]
def stock_research(ticker, company_name):
"""Comprehensive stock research in one function."""
queries = {
"news": f"{ticker} {company_name} stock news today 2026",
"sec_filings": f"{company_name} SEC filing 10-K 10-Q 2026",
"earnings": f"{company_name} earnings call transcript Q1 2026",
"competitors": f"{company_name} competitors market share analysis",
"analyst": f"{ticker} analyst rating price target 2026",
"risks": f"{company_name} risk factors regulatory concerns 2026",
}
research = {}
for category, query in queries.items():
results = financial_search(query, count=5)
research[category] = [
{"title": r["title"], "url": r["url"],
"snippet": r["description"]}
for r in results
]
print(f"{category}: {len(results)} sources found")
return research
# 6 queries = 6 credits = $0.03 for comprehensive stock research
data = stock_research("NVDA", "NVIDIA")Building the financial MCP server
from mcp.server import Server
from mcp.types import Tool, TextContent
import requests, os, json
app = Server("financial-data")
SCAVIO_KEY = os.environ["SCAVIO_API_KEY"]
def search(query, count=10):
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": SCAVIO_KEY},
json={"query": query, "num_results": count}
)
return resp.json()["results"]
@app.tool()
async def stock_news(ticker: str, company: str) -> list[TextContent]:
"""Get latest news for a stock ticker."""
results = search(f"{ticker} {company} stock news 2026", count=5)
formatted = "\n".join([
f"- {r['title']}: {r['description'][:150]} ({r['url']})"
for r in results
])
return [TextContent(type="text", text=formatted)]
@app.tool()
async def sec_filings(company: str, filing_type: str = "10-K") -> list[TextContent]:
"""Search for SEC filings for a company."""
results = search(
f"{company} SEC {filing_type} filing site:sec.gov OR site:edgar", count=5
)
formatted = "\n".join([
f"- {r['title']}: {r['url']}" for r in results
])
return [TextContent(type="text", text=formatted)]
@app.tool()
async def competitor_analysis(company: str) -> list[TextContent]:
"""Analyze competitors and market position."""
results = search(
f"{company} competitors market share revenue comparison 2026", count=8
)
formatted = "\n".join([
f"- {r['title']}: {r['description'][:150]}" for r in results
])
return [TextContent(type="text", text=formatted)]
@app.tool()
async def earnings_data(company: str, quarter: str = "latest") -> list[TextContent]:
"""Get earnings call data and transcripts."""
results = search(
f"{company} earnings call transcript {quarter} 2026", count=5
)
formatted = "\n".join([
f"- {r['title']}: {r['description'][:150]} ({r['url']})"
for r in results
])
return [TextContent(type="text", text=formatted)]
@app.tool()
async def risk_analysis(company: str) -> list[TextContent]:
"""Identify risk factors and regulatory concerns."""
results = search(
f"{company} risk factors regulatory lawsuit antitrust 2026", count=5
)
formatted = "\n".join([
f"- {r['title']}: {r['description'][:150]}" for r in results
])
return [TextContent(type="text", text=formatted)]Tavily vs broader search for financial queries
- Stock news: both work well. Tavily and general search APIs both return timely news articles.
- SEC filings: Tavily often misses direct SEC.gov results. A broader search API includes EDGAR results because search engines index them.
- Earnings transcripts: Tavily returns summaries from financial blogs. Broader search finds actual transcript sources (Seeking Alpha, earnings call archives).
- Competitor analysis: Tavily limited to recent articles. Broader search includes industry reports, market research summaries, and analyst notes.
- Risk factors: both serviceable, but broader search captures regulatory filings and legal documents that Tavily's index sometimes skips.
Cost comparison for financial research
# Cost per stock research session (6 queries)
providers = {
"Tavily (free)": {"limit": 1000, "cost_per_query": 0},
"Tavily ($30/mo)": {"limit": 10000, "cost_per_query": 0.003},
"Scavio ($30/mo)": {"limit": 7000, "cost_per_query": 0.005},
}
queries_per_stock = 6
stocks_per_day = 5
monthly_queries = queries_per_stock * stocks_per_day * 22 # work days
for name, p in providers.items():
if monthly_queries <= p["limit"]:
cost = 30 if "30" in name else 0
else:
overage = monthly_queries - p["limit"]
cost = 30 + (overage * p["cost_per_query"])
print(f"{name}: {monthly_queries} queries/mo = ${cost:.2f}/mo")
# 660 queries/month covers 5 stocks daily with deep research
# Both Tavily and Scavio handle this within their $30 plansMCP configuration
{
"mcpServers": {
"financial-data": {
"command": "python",
"args": ["financial_mcp_server.py"],
"env": {
"SCAVIO_API_KEY": "your-key-here"
}
}
}
}The financial MCP server pattern works with any search backend, but the breadth of results matters more for financial research than for general queries. SEC filings, earnings transcripts, and regulatory documents are indexed by search engines but often missed by search APIs that optimize only for news and blog content. A broader search API turns a stock-news-only MCP into a comprehensive financial research tool for the same cost.