aster-info-mcp

Instructions

Fetch recent trades data from Aster Finance API and return as Markdown table text.

Parameters:
    symbol (str): Trading pair symbol (e.g., 'BTCUSDT', 'ETHUSDT'). Case-insensitive.
    limit (Optional[int]): Number of trades to return (1 to 1000). If None, defaults to 500.

Returns:
    str: Markdown table containing tradeId, price, qty, quoteQty, time, and isBuyerMaker.

Raises:
    Exception: If the API request fails or data processing encounters an error.

Implementation Reference

• main.py:627-685 (handler)

  The handler function for the 'get_recent_trades' tool. It fetches recent trade data from the Aster Finance API (/fapi/v1/trades endpoint), processes it using pandas into a formatted Markdown table showing trade ID, price, quantity, quote quantity, time, and whether the buyer was the maker.

  async def get_recent_trades(
      symbol: str,
      limit: Optional[int] = None
  ) -> str:
      """
      Fetch recent trades data from Aster Finance API and return as Markdown table text.
      
      Parameters:
          symbol (str): Trading pair symbol (e.g., 'BTCUSDT', 'ETHUSDT'). Case-insensitive.
          limit (Optional[int]): Number of trades to return (1 to 1000). If None, defaults to 500.
      
      Returns:
          str: Markdown table containing tradeId, price, qty, quoteQty, time, and isBuyerMaker.
      
      Raises:
          Exception: If the API request fails or data processing encounters an error.
      """
      endpoint = "/fapi/v1/trades"
      
      # Construct query parameters
      params = {
          "symbol": symbol.upper(),  # Ensure symbol is uppercase (e.g., BTCUSDT)
      }
      if limit is not None:
          params["limit"] = limit
  
      async with httpx.AsyncClient() as client:
          try:
              # Make GET request to the API
              response = await client.get(f"{BASE_URL}{endpoint}", params=params)
              response.raise_for_status()  # Raise exception for 4xx/5xx errors
              
              # Parse JSON response
              trades_data = response.json()
              
              # Create pandas DataFrame
              df = pd.DataFrame(trades_data)
              
              # Convert time to readable format
              df["time"] = pd.to_datetime(df["time"], unit="ms")
              
              # Select relevant columns and format numbers
              df = df[["id", "price", "qty", "quoteQty", "time", "isBuyerMaker"]]
              df = df.rename(columns={"id": "tradeId"})  # Rename id to tradeId for clarity
              df["price"] = df["price"].astype(float).round(8)
              df["qty"] = df["qty"].astype(float).round(8)
              df["quoteQty"] = df["quoteQty"].astype(float).round(8)
              
              # Convert DataFrame to Markdown table
              markdown_table = df.to_markdown(index=False)
              
              return markdown_table
          
          except httpx.HTTPStatusError as e:
              # Handle HTTP errors (e.g., 400, 429)
              raise Exception(f"API request failed: {e.response.status_code} - {e.response.text}")
          except Exception as e:
              # Handle other errors (e.g., network issues, pandas errors)
              raise Exception(f"Error processing recent trades data: {str(e)}")