Update custom_tools.py

custom_tools.py

@@ -1,371 +1,371 @@
-import os
-from langchain_community.tools import DuckDuckGoSearchResults, RedditSearchRun
-from langchain_community.utilities.reddit_search import RedditSearchAPIWrapper
-from langchain_community.tools.reddit_search.tool import RedditSearchSchema
-from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
-from langchain.tools import Tool , tool
-from pydantic import BaseModel
-from time import sleep
-import re
-
-groq_api= os.getenv('GROQ_API_KEY')
-Onews_api =  os.getenv('NEWS_API')
-
-
-from newsdataapi import NewsDataApiClient
-import yfinance as yf
-import pandas as pd
-
-
-class RedditInput(BaseModel):
-    query: str
-    sort: str = "new"
-    time_filter: str = "week"
-    subreddit: str = "stocks"
-    limit: str = "5"
-
-class WebSearchInput(BaseModel):
-    query: str
-    
-class StanderdNewsSearchProtocol(BaseModel):
-    topic: str
-    
-
-class StockFundamentals(BaseModel):
-    company_name: str
-
-
-
-@tool(args_schema=RedditInput)
-def reddit_search_tool(query: str, sort: str, time_filter: str, subreddit: str, limit: str) -> str:
-    """
-    Search Reddit for a given query. Provide query and optionally sort, time_filter, subreddit, and limit.
-    """
-    sleep(1)
-    try: 
-        search = RedditSearchRun(api_wrapper=RedditSearchAPIWrapper())
-        search_params = RedditSearchSchema(
-            query=query, 
-            sort=sort, 
-            time_filter=time_filter, 
-            subreddit=subreddit, 
-            limit=limit
-        )
-        result = search.run(tool_input=search_params.model_dump())
-    except Exception as e:
-        result = "There was an error in ruuning the tool. try again or skip the tool"
-    
-    sleep(1)
-    return result
-
-
-def resolve_ticker(company_name: str) -> str:
-    """
-    Resolves the correct stock ticker for a given company name using web search.
-    Example: 'Apple' -> 'AAPL', 'Tesla' -> 'TSLA'
-    """
-    try:
-        wrapper = DuckDuckGoSearchAPIWrapper(max_results=1)
-        search = DuckDuckGoSearchResults(api_wrapper=wrapper)
-        query = f"{company_name} stock ticker site:finance.yahoo.com"
-        results = search.invoke(query)
-        match = re.search(r"finance\.yahoo\.com/quote/([^/?]+)", results)
-        if match:
-            return match.group(1).strip()
-        else : return f"Not able to find the correct stocks name for {company_name}. Trying again..."
-    except :
-        return "Not able to run the tool successfuly."
-
-
-
-@tool(args_schema=StockFundamentals)
-def fetch_stock_summary(company_name: str) -> str:
-    """
-    Fetches a comprehensive stock summary including technical indicators, daily stats for the last 4 days,
-    1-month summary, and quarterly trends.
-     Args: company_name: Full name of the company.
-    """
-    sleep(1)
-    try:
-        ticker =  resolve_ticker(company_name=company_name)
-        stock =  yf.Ticker(ticker)
-        info = stock.info
-        current_price = info.get("currentPrice", "N/A")
-        market_cap = info.get("marketCap", "N/A")
-        pe_ratio = info.get("trailingPE", "N/A")
-        sector = info.get("sector", "N/A")
-        industry = info.get("industry", "N/A")
-        summary = info.get("longBusinessSummary", "N/A")
-
-        last_4_days = stock.history(period="5d")
-        last_4 = last_4_days.tail(4).copy()
-        daily_info = "\nLast 4 Days:\n"
-        for date, row in last_4.iterrows():
-            change = ((row['Close'] - row['Open']) / row['Open']) * 100
-            daily_info += f"- {date.date()}: Close ${row['Close']:.2f}, Vol: {int(row['Volume'])}, Change: {change:+.2f}%\n"
-
-        month_df = stock.history(period="1mo")
-        avg_close = month_df['Close'].mean()
-        high_close = month_df['Close'].max()
-        low_close = month_df['Close'].min()
-        total_volume = month_df['Volume'].sum()
-        month_summary = (
-            f"\n1-Month Summary:\n"
-            f"- Avg Close: ${avg_close:.2f}\n"
-            f"- High: ${high_close:.2f} | Low: ${low_close:.2f}\n"
-            f"- Total Volume: {int(total_volume)}"
-        )
-
-        quarter_df = stock.history(period="3mo")
-        start_price = quarter_df['Close'].iloc[0]
-        end_price = quarter_df['Close'].iloc[-1]
-        pct_change = ((end_price - start_price) / start_price) * 100
-        high_q = quarter_df['Close'].max()
-        low_q = quarter_df['Close'].min()
-        avg_vol_q = quarter_df['Volume'].mean()
-        quarter_summary = (
-            f"\nQuarterly Summary (3mo):\n"
-            f"- Start Price: ${start_price:.2f} | End Price: ${end_price:.2f}\n"
-            f"- % Change: {pct_change:.2f}%\n"
-            f"- High: ${high_q:.2f} | Low: ${low_q:.2f}\n"
-            f"- Avg Volume: {int(avg_vol_q)}"
-        )
-
-        df = month_df.copy()
-        df['SMA_10'] = df['Close'].rolling(10).mean()
-        df['EMA_10'] = df['Close'].ewm(span=10).mean()
-        delta = df['Close'].diff()
-        gain = delta.where(delta > 0, 0.0)
-        loss = -delta.where(delta < 0, 0.0)
-        avg_gain = gain.rolling(window=14).mean()
-        avg_loss = loss.rolling(window=14).mean()
-        rs = avg_gain / avg_loss
-        df['RSI_14'] = 100 - (100 / (1 + rs))
-        ema_12 = df['Close'].ewm(span=12, adjust=False).mean()
-        ema_26 = df['Close'].ewm(span=26, adjust=False).mean()
-        df['MACD'] = ema_12 - ema_26
-        df['MACD_Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
-        df['BB_Middle'] = df['Close'].rolling(20).mean()
-        df['BB_Upper'] = df['BB_Middle'] + 2 * df['Close'].rolling(20).std()
-        df['BB_Lower'] = df['BB_Middle'] - 2 * df['Close'].rolling(20).std()
-        df['ATR_14'] = df[['High', 'Low', 'Close']].apply(lambda x: max(x['High'] - x['Low'], abs(x['High'] - x['Close']), abs(x['Low'] - x['Close'])), axis=1).rolling(14).mean()
-        df['Volatility'] = df['Close'].pct_change().rolling(14).std()
-        latest = df.iloc[-1]
-
-        indicators = (
-            f"\nTechnical Indicators:\n"
-            f"- SMA(10): {latest['SMA_10']:.2f} | EMA(10): {latest['EMA_10']:.2f}\n"
-            f"- RSI(14): {latest['RSI_14']:.2f}\n"
-            f"- MACD: {latest['MACD']:.2f} | Signal: {latest['MACD_Signal']:.2f}\n"
-            f"- Bollinger Bands: Upper={latest['BB_Upper']:.2f}, Lower={latest['BB_Lower']:.2f}\n"
-            f"- ATR(14): {latest['ATR_14']:.2f}\n"
-            f"- Volatility (14-day): {latest['Volatility']:.4f}"
-        )
-
-        output = (
-            f"{ticker.upper()} Summary:\n"
-            f"- Current Price: ${current_price}\n"
-            f"- Market Cap: {market_cap}\n"
-            f"- Sector: {sector} | Industry: {industry}\n"
-            f"- PE Ratio: {pe_ratio}\n"
-            f"{daily_info}"
-            f"{month_summary}"
-            f"{quarter_summary}"
-            f"{indicators}"
-            f"\n\nCompany Overview:\n{summary}"
-        )
-
-        return output
-    
-    except Exception as e:
-        return f"Error fetching stock data for {company_name}: {str(e)}"
-
-@tool(args_schema=WebSearchInput)
-def web_search(query: str) -> str:
-    """
-    This function allows to search anything on internet. A big query with more details will only give a high quality result.
-    Args: query: Search query.
-    """
-    sleep(1)
-    try:
-        wrapper = DuckDuckGoSearchAPIWrapper(max_results=2)
-        search = DuckDuckGoSearchResults(api_wrapper=wrapper)
-        return search.invoke(query)
-    except:
-        return "Error in running the tool."
-
-@tool(args_schema=StanderdNewsSearchProtocol)
-def tech_news(topic:str) -> str:
-    """
-    Fetches recent UK-based technology news headlines and descriptions from NewsData.io
-    with a focus on the given topic (matched in the article title).
-
-    Args:
-        topic (str): The keyword to search for in technology news article titles.
-
-    Returns:
-        str: A concatenated string of news summaries with topic-specific tech news.
-    """
-    sleep(1)
-    try:
-        client = NewsDataApiClient(apikey=Onews_api,
-                                   debug=True,
-                                   folder_path="./news_output")
-        content = client.latest_api(category="technology", language="en", country="gb", size=3,qInTitle=topic)
-        content = content['results']
-        tech_news= ""
-        for  i, j in enumerate(content):
-            full_news = f"tech_news {i+1}: "+ j["description"]
-            tech_news += full_news    
-        return tech_news
-    except:
-        return "There was an error. Can't run the tool"
-
-@tool(args_schema=StanderdNewsSearchProtocol)
-def politics_news(topic:str) -> str:
-    """
-    Fetches recent UK-based politics news headlines and descriptions from NewsData.io
-    with a focus on the given topic (matched in the article title).
-
-    Args:
-        topic (str): The keyword to search for in politics news article titles.
-
-    Returns:
-        str: A concatenated string of news summaries with topic-specific political news.
-    """
-    sleep(1)
-    try:
-        
-        client = NewsDataApiClient(apikey=Onews_api,
-                                   debug=True,
-                                   folder_path="./news_output")
-        content = client.latest_api(category="politics", language="en", country="gb", size=3,qInTitle=topic)
-        content = content['results']
-        p_news= ""
-        for  i, j in enumerate(content):
-            full_news = f"politics_news {i+1}: "+ j["description"]
-            p_news += full_news    
-        return p_news
-    except:
-        return "There was an error. Can't run the tool"
-
-@tool(args_schema=StanderdNewsSearchProtocol)
-def business_news(topic:str) -> str:
-    """
-    Fetches recent UK-based business news headlines and descriptions from NewsData.io
-    with a focus on the given topic (matched in the article title).
-
-    Args:
-        topic (str): The keyword to search for in business news article titles.
-
-    Returns:
-        str: A concatenated string of news summaries with topic-specific business news.
-    """
-    sleep(1)
-    try:
-        client = NewsDataApiClient(apikey=Onews_api,
-                                   debug=True,
-                                   folder_path="./news_output")
-        content = client.latest_api(category="business", language="en", country="gb", size=3,qInTitle=topic)
-        content = content['results']
-        b_news= ""
-        for  i, j in enumerate(content):
-            full_news = f"business_news {i+1}: "+ j["description"]
-            b_news += full_news    
-        return b_news
-    except:
-        return "There was an error. Can't run the tool"
-
-@tool(args_schema=StanderdNewsSearchProtocol)
-def world_news(topic:str) -> str:
-    """
-    Fetches recent world news headlines related to UK and descriptions from NewsData.io
-    with a focus on the given topic (matched in the article title).
-
-    Args:
-        topic (str): The keyword to search for in World news article titles.
-
-    Returns:
-        str: A concatenated string of news summaries with topic-specific world news.
-    """
-    sleep(1)
-    try:
-        client = NewsDataApiClient(apikey=Onews_api,
-                                   debug=True,
-                                   folder_path="./news_output")
-        content = client.latest_api(category="world", language="en", country="gb", size=3,qInTitle=topic)
-        content = content['results']
-        w_news= ""
-        for  i, j in enumerate(content):
-            full_news = f"world_news {i+1}: "+ j["description"]
-            w_news += full_news    
-        return w_news
-    except:
-        return "There was an error. Can't run the tool"
-
-stock_data_tool = Tool(
-    name="Stock Market Data",
-    func=fetch_stock_summary,
-    description=(
-        "Use this tool to get current stock market data like price, market cap, "
-        "and historical trend for a specific stock ticker (e.g., AAPL, NVDA, TSLA)."
-    )
-)
-
-web_search = Tool(
-    name="Web Search",
-    func=web_search,
-    description=(
-        "Use this tool to Search and get any general information from the Internet about the stock. This tool takes a query and returns the result." 
-        "For high Quality results provide a good length query with as much details as posible."
-    )
-)
-
-reddit_search_tool = Tool(
-    name="Reddit Search",
-    func=reddit_search_tool,
-    description=(
-        "Use this tool to search Reddit for recent discussions and sentiments about a stock, event, or topic."
-        "Input should be a search query (e.g., 'Do you like tesla?', 'what do you think about Tesla products?' , 'Tesla is a scam')."
-        "Args: query (str): The search query (e.g., 'Tesla stock'). sort (str): Sort order ('new', 'hot', etc.). Defaults to 'new'. time_filter (str): Time range ('hour', 'day', 'week', 'month', 'year', 'all'). Defaults to 'week'. subreddit (str): type of subreddit ('stocks', 'products', 'car', 'bikes'). limit (str): Maximum number of results to return. Defaults to '10'."
-    )
-)
-
-
-tech_news_tool = Tool(
-    name="Technology News Search",
-    func=tech_news,
-    description=("Use this tool to get the latest technology news articles from the UK that match a topic (e.g., AI, robotics, fintech, Apple, Meta, Tesla).")
-)
-
-
-politics_news_tool = Tool(
-    name="Politics News Search",
-    func=politics_news,
-    description=("Use this tool to get the latest politicial news articles from the UK that match a topic (e.g., AI, robotics, fintech, Apple, Meta, Tesla).")
-)
-
-business_news_tool = Tool(
-    name="Business News Search",
-    func=business_news,
-    description=("Use this tool to get the latest Business news articles from the UK that match a topic (e.g., AI, robotics, fintech, Apple, Meta, Tesla).")
-)
-
-world_news_tool = Tool(
-    name="World News Search",
-    func=world_news,
-    description=("Use this tool to get the latest World news (geopolitical) articles from the UK that match a topic (e.g., AI, robotics, fintech, Apple, Meta, Tesla).")
-)
-
-
-
-def get_tools():
-    return [
-        stock_data_tool,
-        reddit_search_tool,
-        web_search,
-        tech_news_tool,
-        business_news_tool,
-        politics_news_tool,
-        world_news_tool
+import os
+from langchain_community.tools import DuckDuckGoSearchResults, RedditSearchRun
+from langchain_community.utilities.reddit_search import RedditSearchAPIWrapper
+from langchain_community.tools.reddit_search.tool import RedditSearchSchema
+from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
+from langchain.tools import Tool , tool
+from pydantic import BaseModel
+from time import sleep
+import re
+
+groq_api= os.getenv('GROQ_API_KEY')
+Onews_api =  os.getenv('NEWS_API')
+
+
+from newsdataapi import NewsDataApiClient
+import yfinance as yf
+import pandas as pd
+
+
+class RedditInput(BaseModel):
+    query: str
+    sort: str = "new"
+    time_filter: str = "week"
+    subreddit: str = "stocks"
+    limit: str = "5"
+
+class WebSearchInput(BaseModel):
+    query: str
+    
+class StanderdNewsSearchProtocol(BaseModel):
+    topic: str
+    
+
+class StockFundamentals(BaseModel):
+    company_name: str
+
+
+
+@tool(args_schema=RedditInput)
+def reddit_search_tool(query: str, sort: str, time_filter: str, subreddit: str, limit: str) -> str:
+    """
+    Search Reddit for a given query. Provide query and optionally sort, time_filter, subreddit, and limit.
+    """
+    sleep(1)
+    try: 
+        search = RedditSearchRun(api_wrapper=RedditSearchAPIWrapper())
+        search_params = RedditSearchSchema(
+            query=query, 
+            sort=sort, 
+            time_filter=time_filter, 
+            subreddit=subreddit, 
+            limit=limit
+        )
+        result = search.run(tool_input=search_params.model_dump())
+    except Exception as e:
+        result = "There was an error in ruuning the tool. try again or skip the tool"
+    
+    sleep(1)
+    return result
+
+
+def resolve_ticker(company_name: str) -> str:
+    """
+    Resolves the correct stock ticker for a given company name using web search.
+    Example: 'Apple' -> 'AAPL', 'Tesla' -> 'TSLA'
+    """
+    try:
+        wrapper = DuckDuckGoSearchAPIWrapper(max_results=1)
+        search = DuckDuckGoSearchResults(api_wrapper=wrapper)
+        query = f"{company_name} stock ticker site:finance.yahoo.com"
+        results = search.invoke(query)
+        match = re.search(r"finance\.yahoo\.com/quote/([^/?]+)", results)
+        if match:
+            return match.group(1).strip()
+        else : return f"Not able to find the correct stocks name for {company_name}. Trying again..."
+    except :
+        return "Not able to run the tool successfuly."
+
+
+
+@tool(args_schema=StockFundamentals)
+def fetch_stock_summary(company_name: str) -> str:
+    """
+    Fetches a comprehensive stock summary including technical indicators, daily stats for the last 4 days,
+    1-month summary, and quarterly trends.
+     Args: company_name: Full name of the company.
+    """
+    sleep(1)
+    try:
+        ticker =  resolve_ticker(company_name=company_name)
+        stock =  yf.Ticker(ticker)
+        info = stock.info
+        current_price = info.get("currentPrice", "N/A")
+        market_cap = info.get("marketCap", "N/A")
+        pe_ratio = info.get("trailingPE", "N/A")
+        sector = info.get("sector", "N/A")
+        industry = info.get("industry", "N/A")
+        summary = info.get("longBusinessSummary", "N/A")
+
+        last_4_days = stock.history(period="5d")
+        last_4 = last_4_days.tail(4).copy()
+        daily_info = "\nLast 4 Days:\n"
+        for date, row in last_4.iterrows():
+            change = ((row['Close'] - row['Open']) / row['Open']) * 100
+            daily_info += f"- {date.date()}: Close ${row['Close']:.2f}, Vol: {int(row['Volume'])}, Change: {change:+.2f}%\n"
+
+        month_df = stock.history(period="1mo")
+        avg_close = month_df['Close'].mean()
+        high_close = month_df['Close'].max()
+        low_close = month_df['Close'].min()
+        total_volume = month_df['Volume'].sum()
+        month_summary = (
+            f"\n1-Month Summary:\n"
+            f"- Avg Close: ${avg_close:.2f}\n"
+            f"- High: ${high_close:.2f} | Low: ${low_close:.2f}\n"
+            f"- Total Volume: {int(total_volume)}"
+        )
+
+        quarter_df = stock.history(period="3mo")
+        start_price = quarter_df['Close'].iloc[0]
+        end_price = quarter_df['Close'].iloc[-1]
+        pct_change = ((end_price - start_price) / start_price) * 100
+        high_q = quarter_df['Close'].max()
+        low_q = quarter_df['Close'].min()
+        avg_vol_q = quarter_df['Volume'].mean()
+        quarter_summary = (
+            f"\nQuarterly Summary (3mo):\n"
+            f"- Start Price: ${start_price:.2f} | End Price: ${end_price:.2f}\n"
+            f"- % Change: {pct_change:.2f}%\n"
+            f"- High: ${high_q:.2f} | Low: ${low_q:.2f}\n"
+            f"- Avg Volume: {int(avg_vol_q)}"
+        )
+
+        df = month_df.copy()
+        df['SMA_10'] = df['Close'].rolling(10).mean()
+        df['EMA_10'] = df['Close'].ewm(span=10).mean()
+        delta = df['Close'].diff()
+        gain = delta.where(delta > 0, 0.0)
+        loss = -delta.where(delta < 0, 0.0)
+        avg_gain = gain.rolling(window=14).mean()
+        avg_loss = loss.rolling(window=14).mean()
+        rs = avg_gain / avg_loss
+        df['RSI_14'] = 100 - (100 / (1 + rs))
+        ema_12 = df['Close'].ewm(span=12, adjust=False).mean()
+        ema_26 = df['Close'].ewm(span=26, adjust=False).mean()
+        df['MACD'] = ema_12 - ema_26
+        df['MACD_Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
+        df['BB_Middle'] = df['Close'].rolling(20).mean()
+        df['BB_Upper'] = df['BB_Middle'] + 2 * df['Close'].rolling(20).std()
+        df['BB_Lower'] = df['BB_Middle'] - 2 * df['Close'].rolling(20).std()
+        df['ATR_14'] = df[['High', 'Low', 'Close']].apply(lambda x: max(x['High'] - x['Low'], abs(x['High'] - x['Close']), abs(x['Low'] - x['Close'])), axis=1).rolling(14).mean()
+        df['Volatility'] = df['Close'].pct_change().rolling(14).std()
+        latest = df.iloc[-1]
+
+        indicators = (
+            f"\nTechnical Indicators:\n"
+            f"- SMA(10): {latest['SMA_10']:.2f} | EMA(10): {latest['EMA_10']:.2f}\n"
+            f"- RSI(14): {latest['RSI_14']:.2f}\n"
+            f"- MACD: {latest['MACD']:.2f} | Signal: {latest['MACD_Signal']:.2f}\n"
+            f"- Bollinger Bands: Upper={latest['BB_Upper']:.2f}, Lower={latest['BB_Lower']:.2f}\n"
+            f"- ATR(14): {latest['ATR_14']:.2f}\n"
+            f"- Volatility (14-day): {latest['Volatility']:.4f}"
+        )
+
+        output = (
+            f"{ticker.upper()} Summary:\n"
+            f"- Current Price: ${current_price}\n"
+            f"- Market Cap: {market_cap}\n"
+            f"- Sector: {sector} | Industry: {industry}\n"
+            f"- PE Ratio: {pe_ratio}\n"
+            f"{daily_info}"
+            f"{month_summary}"
+            f"{quarter_summary}"
+            f"{indicators}"
+            f"\n\nCompany Overview:\n{summary}"
+        )
+
+        return output
+    
+    except Exception as e:
+        return f"Error fetching stock data for {company_name}: {str(e)}"
+
+@tool(args_schema=WebSearchInput)
+def web_search(query: str) -> str:
+    """
+    This function allows to search anything on internet. A big query with more details will only give a high quality result.
+    Args: query: Search query.
+    """
+    sleep(1)
+    try:
+        wrapper = DuckDuckGoSearchAPIWrapper(max_results=2)
+        search = DuckDuckGoSearchResults(api_wrapper=wrapper)
+        return search.invoke(query)
+    except:
+        return "Error in running the tool."
+
+@tool(args_schema=StanderdNewsSearchProtocol)
+def tech_news(topic:str) -> str:
+    """
+    Fetches recent UK-based technology news headlines and descriptions from NewsData.io
+    with a focus on the given topic (matched in the article title).
+
+    Args:
+        topic (str): The keyword to search for in technology news article titles.
+
+    Returns:
+        str: A concatenated string of news summaries with topic-specific tech news.
+    """
+    sleep(1)
+    try:
+        client = NewsDataApiClient(apikey=Onews_api,
+                                   debug=True,
+                                   folder_path="./news_output")
+        content = client.latest_api(category="technology", language="en", country="gb", size=3,qInTitle=topic)
+        content = content['results']
+        tech_news= ""
+        for  i, j in enumerate(content):
+            full_news = f"tech_news {i+1}: "+ j["description"]
+            tech_news += full_news    
+        return tech_news
+    except:
+        return "There was an error. Can't run the tool"
+
+@tool(args_schema=StanderdNewsSearchProtocol)
+def politics_news(topic:str) -> str:
+    """
+    Fetches recent UK-based politics news headlines and descriptions from NewsData.io
+    with a focus on the given topic (matched in the article title).
+
+    Args:
+        topic (str): The keyword to search for in politics news article titles.
+
+    Returns:
+        str: A concatenated string of news summaries with topic-specific political news.
+    """
+    sleep(1)
+    try:
+        
+        client = NewsDataApiClient(apikey=Onews_api,
+                                   debug=True,
+                                   folder_path="./news_output")
+        content = client.latest_api(category="politics", language="en", country="gb", size=3,qInTitle=topic)
+        content = content['results']
+        p_news= ""
+        for  i, j in enumerate(content):
+            full_news = f"politics_news {i+1}: "+ j["description"]
+            p_news += full_news    
+        return p_news
+    except:
+        return "There was an error. Can't run the tool"
+
+@tool(args_schema=StanderdNewsSearchProtocol)
+def business_news(topic:str) -> str:
+    """
+    Fetches recent UK-based business news headlines and descriptions from NewsData.io
+    with a focus on the given topic (matched in the article title).
+
+    Args:
+        topic (str): The keyword to search for in business news article titles.
+
+    Returns:
+        str: A concatenated string of news summaries with topic-specific business news.
+    """
+    sleep(1)
+    try:
+        client = NewsDataApiClient(apikey=Onews_api,
+                                   debug=True,
+                                   folder_path="./news_output")
+        content = client.latest_api(category="business", language="en", country="gb", size=3,qInTitle=topic)
+        content = content['results']
+        b_news= ""
+        for  i, j in enumerate(content):
+            full_news = f"business_news {i+1}: "+ j["description"]
+            b_news += full_news    
+        return b_news
+    except:
+        return "There was an error. Can't run the tool"
+
+@tool(args_schema=StanderdNewsSearchProtocol)
+def world_news(topic:str) -> str:
+    """
+    Fetches recent world news headlines related to UK and descriptions from NewsData.io
+    with a focus on the given topic (matched in the article title).
+
+    Args:
+        topic (str): The keyword to search for in World news article titles.
+
+    Returns:
+        str: A concatenated string of news summaries with topic-specific world news.
+    """
+    sleep(1)
+    try:
+        client = NewsDataApiClient(apikey=Onews_api,
+                                   debug=True,
+                                   folder_path="./news_output")
+        content = client.latest_api(category="world", language="en", country="gb", size=3,qInTitle=topic)
+        content = content['results']
+        w_news= ""
+        for  i, j in enumerate(content):
+            full_news = f"world_news {i+1}: "+ j["description"]
+            w_news += full_news    
+        return w_news
+    except:
+        return "There was an error. Can't run the tool"
+
+stock_data_tool = Tool(
+    name="Stock Market Data",
+    func=fetch_stock_summary,
+    description=(
+        "Use this tool to get current stock market data like price, market cap, and historical trend for a specific Company. (e.g., apple or APPLE, NVIDIA or nvidia, TESLA or tesla)."
+        "Args: company_name (str): the name of the company (e.g., 'Tesla')"
+    )
+)
+
+web_search = Tool(
+    name="Web Search",
+    func=web_search,
+    description=(
+        "Use this tool to Search and get any general information from the Internet about the stock. This tool takes a query and returns the result." 
+        "For high Quality results provide a good length query with as much details as posible."
+    )
+)
+
+reddit_search_tool = Tool(
+    name="Reddit Search",
+    func=reddit_search_tool,
+    description=(
+        "Use this tool to search Reddit for recent discussions and sentiments about a stock, event, or topic."
+        "Input should be a search query (e.g., 'Do you like tesla?', 'what do you think about Tesla products?' , 'Tesla is a scam')."
+        "Args: query (str): The search query (e.g., 'Tesla stock'). sort (str): Sort order ('new', 'hot', etc.). Defaults to 'new'. time_filter (str): Time range ('hour', 'day', 'week', 'month', 'year', 'all'). Defaults to 'week'. subreddit (str): type of subreddit ('stocks', 'products', 'car', 'bikes'). limit (str): Maximum number of results to return. Defaults to '10'."
+    )
+)
+
+
+tech_news_tool = Tool(
+    name="Technology News Search",
+    func=tech_news,
+    description=("Use this tool to get the latest technology news articles from the UK that match a topic (e.g., AI, robotics, fintech, Apple, Meta, Tesla).")
+)
+
+
+politics_news_tool = Tool(
+    name="Politics News Search",
+    func=politics_news,
+    description=("Use this tool to get the latest politicial news articles from the UK that match a topic (e.g., AI, robotics, fintech, Apple, Meta, Tesla).")
+)
+
+business_news_tool = Tool(
+    name="Business News Search",
+    func=business_news,
+    description=("Use this tool to get the latest Business news articles from the UK that match a topic (e.g., AI, robotics, fintech, Apple, Meta, Tesla).")
+)
+
+world_news_tool = Tool(
+    name="World News Search",
+    func=world_news,
+    description=("Use this tool to get the latest World news (geopolitical) articles from the UK that match a topic (e.g., AI, robotics, fintech, Apple, Meta, Tesla).")
+)
+
+
+
+def get_tools():
+    return [
+        stock_data_tool,
+        reddit_search_tool,
+        web_search,
+        tech_news_tool,
+        business_news_tool,
+        politics_news_tool,
+        world_news_tool
     ]