NFL Transactions MCP

import requests import pandas as pd from bs4 import BeautifulSoup import logging from typing import Dict, List, Optional, Union, Any import json import os import datetime # Set up logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) # Constants BASE_URL = "https://www.prosportstransactions.com/football/Search/SearchResults.php" TEAM_MAPPING = { "49ers": "San Francisco 49ers", "Bears": "Chicago Bears", "Bengals": "Cincinnati Bengals", "Bills": "Buffalo Bills", "Broncos": "Denver Broncos", "Browns": "Cleveland Browns", "Buccaneers": "Tampa Bay Buccaneers", "Cardinals": "Arizona Cardinals", "Chargers": "Los Angeles Chargers", "Chiefs": "Kansas City Chiefs", "Colts": "Indianapolis Colts", "Cowboys": "Dallas Cowboys", "Dolphins": "Miami Dolphins", "Eagles": "Philadelphia Eagles", "Falcons": "Atlanta Falcons", "Giants": "New York Giants", "Jaguars": "Jacksonville Jaguars", "Jets": "New York Jets", "Lions": "Detroit Lions", "Packers": "Green Bay Packers", "Panthers": "Carolina Panthers", "Patriots": "New England Patriots", "Raiders": "Las Vegas Raiders", "Rams": "Los Angeles Rams", "Ravens": "Baltimore Ravens", "Commanders": "Washington Commanders", "Saints": "New Orleans Saints", "Seahawks": "Seattle Seahawks", "Steelers": "Pittsburgh Steelers", "Texans": "Houston Texans", "Titans": "Tennessee Titans", "Vikings": "Minnesota Vikings", } TRANSACTION_TYPE_MAPPING = { "Player": "PlayerMovementChkBx", "Injury": "InjuriesChkBx", "Legal": "LegalChkBx", "Disciplinary": "DisciplinaryChkBx", "MinorLeague": "MinorLeagueChkBx", "All": None } def validate_date(date_str: str) -> bool: """Validate date string format (YYYY-MM-DD)""" try: datetime.datetime.strptime(date_str, '%Y-%m-%d') return True except ValueError: return False def get_request_params( team: Optional[str] = None, player: Optional[str] = None, start_date: str = "", end_date: str = "", transaction_type: str = "All" ) -> Dict[str, str]: """ Generate request parameters for the ProSportsTransactions search. Args: team: Team name (optional) player: Player name (optional) start_date: Start date in YYYY-MM-DD format end_date: End date in YYYY-MM-DD format transaction_type: Type of transaction to filter Returns: Dictionary of request parameters """ # Validate dates if not validate_date(start_date) or not validate_date(end_date): raise ValueError("Dates must be in YYYY-MM-DD format") # Base params params = { "Player": player or "", "Team": team or "", "BeginDate": start_date, "EndDate": end_date, "submit": "Search" } # Add transaction type checkbox if not "All" tx_type = TRANSACTION_TYPE_MAPPING.get(transaction_type) if tx_type: params[tx_type] = "yes" return params def fetch_page(params: Dict[str, str], page: int = 1) -> Optional[str]: """ Fetch a single page from ProSportsTransactions. Args: params: Search parameters page: Page number Returns: HTML content or None if failed """ if page > 1: params["start"] = str((page - 1) * 25) try: logger.debug(f"Fetching page {page} with params: {params}") response = requests.get(BASE_URL, params=params) response.raise_for_status() return response.text except requests.RequestException as e: logger.error(f"Error fetching page {page}: {e}") return None def has_next_page(html: str) -> bool: """Check if there's a next page based on the HTML content""" soup = BeautifulSoup(html, 'lxml') next_link = soup.select_one('a.next') return next_link is not None def parse_transactions_table(html: str, transaction_type: str) -> pd.DataFrame: """ Parse the HTML table of transactions. Args: html: HTML content transaction_type: Type of transaction (for tagging) Returns: DataFrame of transaction data """ try: # Parse the table using pandas tables = pd.read_html(html) if not tables: logger.warning("No tables found in HTML") return pd.DataFrame() # Get the main table (should be the first one) df = tables[0] # Clean up column names df.columns = [col.strip() for col in df.columns] # Add transaction type column df['transaction_type'] = transaction_type return df except Exception as e: logger.error(f"Error parsing transactions table: {e}") return pd.DataFrame() def fetch_all_transactions( team: Optional[str] = None, player: Optional[str] = None, start_date: str = "", end_date: str = "", transaction_type: str = "All" ) -> pd.DataFrame: """ Fetch all transactions matching the criteria, handling pagination. Args: team: Team name (optional) player: Player name (optional) start_date: Start date in YYYY-MM-DD format end_date: End date in YYYY-MM-DD format transaction_type: Type of transaction to filter Returns: DataFrame containing all transactions """ logger.info(f"Fetching transactions: {start_date} to {end_date}, team={team}, player={player}, type={transaction_type}") params = get_request_params(team, player, start_date, end_date, transaction_type) all_transactions = [] page = 1 while True: html = fetch_page(params, page) if not html: break df = parse_transactions_table(html, transaction_type) if df.empty: break all_transactions.append(df) if not has_next_page(html): break page += 1 # Combine all dataframes if not all_transactions: logger.warning("No transactions found") return pd.DataFrame() result = pd.concat(all_transactions, ignore_index=True) logger.info(f"Found {len(result)} transactions") return result def get_all_nfl_teams() -> List[str]: """Return a list of all NFL teams""" return list(TEAM_MAPPING.values()) def get_available_transaction_types() -> List[str]: """Return a list of available transaction types""" return list(TRANSACTION_TYPE_MAPPING.keys()) def save_to_csv(df: pd.DataFrame, output_path: str) -> str: """Save dataframe to CSV file""" try: df.to_csv(output_path, index=False) return output_path except Exception as e: logger.error(f"Error saving to CSV: {e}") raise def save_to_json(df: pd.DataFrame, output_path: str) -> str: """Save dataframe to JSON file""" try: result = df.to_json(orient='records') with open(output_path, 'w') as f: f.write(result) return output_path except Exception as e: logger.error(f"Error saving to JSON: {e}") raise