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6cd9238 dff71a5 6cd9238 dff71a5 6cd9238 dff71a5 6cd9238 dff71a5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | from mcp.server.fastmcp import FastMCP
import gnews
from bs4 import BeautifulSoup
import re
import os
port = int(os.environ.get("PORT", "8002"))
mcp = FastMCP(
name="Financial News Server",
description="Provides tools and resources for fetching and analyzing financial news with real-time updates via SSE.",
host="0.0.0.0",
port=port,
stateless_http=True,
)
def preprocess_text(text):
"""A simple function to clean text by removing HTML and extra whitespace."""
if not text:
return ""
soup = BeautifulSoup(text, "html.parser")
clean_text = soup.get_text()
clean_text = re.sub(r"\s+", " ", clean_text).strip()
return clean_text
@mcp.tool()
async def get_latest_news(ticker: str) -> str:
"""
Fetches recent news headlines and descriptions for a specific stock ticker.
Use this tool when a user asks for news, updates, recent events about a company or about the company's stock.
The input must be a valid stock ticker symbol (e.g., 'AAPL', 'GOOG').
"""
try:
google_news = gnews.GNews(language="en", country="US", period="7d")
search_query = f'"{ticker}" stock market news'
articles = google_news.get_news(search_query)
if not articles:
return f"No recent news found for ticker {ticker}."
# Format the top 5 articles into a clean string for the LLM
formatted_response = f"Recent News for {ticker}:\n"
for article in articles[:5]:
title = preprocess_text(article.get("title"))
publisher = article.get("publisher", {}).get("title", "N/A")
formatted_response += f"- {title} (Source: {publisher})\n"
return formatted_response
except Exception as e:
return f"An error occurred while fetching news: {e}"
@mcp.resource("docs://news_sources_guide")
def get_news_sources_guide() -> str:
"""Provides guidance on interpreting financial news sources."""
return """
When analyzing financial news, consider the source's reputation.
- Tier 1 (High Reliability): Reuters, Bloomberg, Wall Street Journal (WSJ). These are professional financial news outlets.
- Tier 2 (Moderate Reliability): Major news networks (e.g., CNBC, Forbes), market analysis sites (e.g., Seeking Alpha). Often reliable but may have opinion pieces.
- Tier 3 (Variable Reliability): General news aggregators, press releases. Verify information with other sources.
"""
@mcp.prompt()
def summarize_news_sentiment(news_headlines: str) -> str:
"""
Analyzes a block of news headlines and provides a structured sentiment summary.
The input should be a string containing multiple news headlines.
"""
return f"""
Analyze the following financial news headlines and provide a concise summary of the market sentiment.
First, categorize the overall sentiment as 'Positive', 'Negative', or 'Neutral'.
Then, provide a brief one-sentence justification for your choice, citing a key theme from the headlines.
Headlines to analyze:
---
{news_headlines}
---
Your structured summary:
"""
if __name__ == "__main__":
# Use streamable HTTP transport with MCP endpoint
print(f"News Server running on http://localhost:{port}")
print("MCP endpoint available at:")
print(f"- http://localhost:{port}/mcp")
mcp.run(transport="streamable-http")
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