get_order_book_ticker
Fetch real-time order book ticker data for trading pairs from Aster Finance API and display as Markdown tables showing bid/ask prices and quantities.
Instructions
Fetch order book ticker data from Aster Finance API and return as Markdown table text.
Parameters:
symbol (Optional[str]): Trading pair symbol (e.g., 'BTCUSDT', 'ETHUSDT'). Case-insensitive.
If None, returns data for all symbols.
Returns:
str: Markdown table containing symbol, bidPrice, bidQty, askPrice, and askQty.
Raises:
Exception: If the API request fails or data processing encounters an error.Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No |
Implementation Reference
- main.py:494-551 (handler)Handler function for the 'get_order_book_ticker' tool. Fetches best bid/ask prices and quantities from Aster Finance API (/fapi/v1/ticker/bookTicker), processes with pandas DataFrame, formats to Markdown table. Also serves as registration via @mcp.tool() decorator. Input schema via type hints and docstring.
@mcp.tool() async def get_order_book_ticker( symbol: Optional[str] = None ) -> str: """ Fetch order book ticker data from Aster Finance API and return as Markdown table text. Parameters: symbol (Optional[str]): Trading pair symbol (e.g., 'BTCUSDT', 'ETHUSDT'). Case-insensitive. If None, returns data for all symbols. Returns: str: Markdown table containing symbol, bidPrice, bidQty, askPrice, and askQty. Raises: Exception: If the API request fails or data processing encounters an error. """ endpoint = "/fapi/v1/ticker/bookTicker" # Construct query parameters params = {} if symbol is not None: params["symbol"] = symbol.upper() # Ensure symbol is uppercase (e.g., BTCUSDT) 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 ticker_data = response.json() # Handle single symbol (dict) or all symbols (list of dicts) if isinstance(ticker_data, dict): ticker_data = [ticker_data] # Create pandas DataFrame df = pd.DataFrame(ticker_data) # Select relevant columns and format numbers df = df[["symbol", "bidPrice", "bidQty", "askPrice", "askQty"]] df["bidPrice"] = df["bidPrice"].astype(float).round(8) df["bidQty"] = df["bidQty"].astype(float).round(8) df["askPrice"] = df["askPrice"].astype(float).round(8) df["askQty"] = df["askQty"].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 order book ticker data: {str(e)}")