FirstCycling MCP Server

Instructions

Implementation Reference

• FirstCyclingAPI/examples/rider_best_results.py:17-89 (handler)

  The main handler function named exactly 'get_rider_best_results' that takes rider_id, optionally debug, creates Rider object, fetches best_results, and displays top results.
  def get_rider_best_results(rider_id, debug=False): """Get a rider's best results from FirstCycling""" # Create rider instance rider = Rider(rider_id) # Get basic rider info print(f"Rider ID: {rider.ID}") # Debug information if enabled if debug: # Debug: Check the URL directly url = f"https://firstcycling.com/rider.php?r={rider_id}&high=1" print(f"Checking URL: {url}") response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') # Debug: Check for tables tables = soup.find_all('table', {'class': 'tablesorter'}) print(f"Found {len(tables)} tables with class 'tablesorter'") if tables: # Check the first table first_table = tables[0] rows = first_table.find_all('tr') print(f"First table has {len(rows)} rows") if len(rows) > 1: # Header row + at least one data row # Try to parse directly try: print("Table content preview:") for i, row in enumerate(rows[:3]): # Show first 3 rows print(f"Row {i}: {row.get_text().strip()}") # Try direct pandas parsing df = pd.read_html(io.StringIO(str(first_table)), decimal=',')[0] print(f"Successfully parsed table with shape: {df.shape}") print("Column names:", df.columns.tolist()) print("First few rows:") print(df.head(3)) except Exception as e: print(f"Error parsing table directly: {str(e)}") else: print("Table appears to be empty or has only a header row") # Get best results best_results = rider.best_results() # Display information about best results if hasattr(best_results, 'results_df') and not best_results.results_df.empty: print(f"\nFound {len(best_results.results_df)} best results:") # Display the first 10 best results (or all if less than 10) print("\nTop 10 best results:") limit = min(10, len(best_results.results_df)) for i, (_, row) in enumerate(best_results.results_df.head(limit).iterrows(), 1): race = row.get('Race', 'Unknown Race') pos = row.get('Pos', 'N/A') editions = row.get('Editions', '') cat = row.get('CAT', '') country = row.get('Race_Country', '') result_line = f"{i}. {pos}. {race}" if cat: result_line += f" ({cat})" if editions: result_line += f" - {editions}" if country: result_line += f" - {country}" print(result_line) else: print("No best results found for this rider.")
• FirstCyclingAPI/first_cycling_api/rider/rider.py:305-313 (helper)

  Helper method on Rider class that fetches the best results endpoint data using RiderBestResults parser.
  def best_results(self): """ Get the rider's best results. Returns ------- RiderBestResults """ return self._get_endpoint(endpoint=RiderBestResults, high=1)
• FirstCyclingAPI/first_cycling_api/rider/endpoints.py:186-280 (helper)

  Helper class RiderBestResults that parses the HTML soup to extract and structure the best results table into a pandas DataFrame.
  class RiderBestResults(RiderEndpoint): """ Rider's best results. Extends RiderEndpoint. Attributes ---------- results_df : pd.DataFrame Table of rider's best results. """ def _parse_soup(self): super()._parse_soup() self._get_best_results() def _get_best_results(self): # Find table with best results (note different class than victories table) table = self.soup.find('table', {'class': "tablesorter"}) if table: # Check if the table has "No data" content no_data_text = table.get_text().strip() if "No data" in no_data_text: # Table exists but has no data self.results_df = pd.DataFrame() return try: # Try to parse the table manually since the structure is different headers = [th.text.strip() for th in table.find('thead').find_all('th')] # Create empty lists to store row data rows_data = [] # Get all data rows tbody = table.find('tbody') if table.find('tbody') else table for tr in tbody.find_all('tr'): row_data = {} cells = tr.find_all('td') # Skip empty rows if not cells: continue # Map each cell to its header for i, cell in enumerate(cells): if i < len(headers): header = headers[i] row_data[header] = cell.text.strip() # Extract race ID if available if header == 'Race' and cell.find('a'): href = cell.find('a').get('href', '') import re race_id_match = re.search(r'r=(\d+)', href) if race_id_match: row_data['Race_ID'] = race_id_match.group(1) # Extract country code if available if cell.find('img'): img_src = cell.find('img').get('src', '') country_code = img_to_country_code(cell.find('img')) if country_code: row_data['Race_Country'] = country_code rows_data.append(row_data) # Create DataFrame from the collected data self.results_df = pd.DataFrame(rows_data) # If the DataFrame is empty after parsing, set to empty DataFrame if self.results_df.empty: self.results_df = pd.DataFrame() except Exception as e: # If there's an error in parsing, handle it by creating a basic DataFrame manually print(f"Warning: Error parsing best results table: {str(e)}") # Fallback: Try to create a DataFrame directly from the HTML try: # Parse the basic table html = str(table) self.results_df = pd.read_html(io.StringIO(html), decimal=',')[0] # Check if the table contains "No data" if self.results_df.empty or (self.results_df.shape[0] == 1 and any("No data" in str(cell) for cell in self.results_df.iloc[0])): self.results_df = pd.DataFrame() return except Exception as e: # If all else fails, just return an empty DataFrame print(f"Warning: Error creating DataFrame from table HTML: {str(e)}") self.results_df = pd.DataFrame() else: # No table found self.results_df = pd.DataFrame()