aster-info-mcp

Instructions

Implementation Reference

• main.py:311-374 (handler)

  The complete handler function for the 'get_funding_rate_history' tool, decorated with @mcp.tool() for registration. It fetches funding rate history from the Aster Finance API (/fapi/v1/fundingRate), processes the data using pandas into a formatted Markdown table, with input parameters defining the schema via type hints and docstring.
  async def get_funding_rate_history( symbol: str, startTime: Optional[int] = None, endTime: Optional[int] = None, limit: Optional[int] = None ) -> str: """ Fetch Funding Rate History data from Aster Finance API and return as Markdown table text. Parameters: symbol (str): Trading pair symbol (e.g., 'BTCUSDT', 'ETHUSDT'). Case-insensitive. startTime (Optional[int]): Start time in milliseconds since Unix epoch. If None, defaults to API behavior. endTime (Optional[int]): End time in milliseconds since Unix epoch. If None, defaults to API behavior. limit (Optional[int]): Number of funding rate records to return (1 to 1000). If None, defaults to 100. Returns: str: Markdown table containing symbol, fundingTime, and fundingRate. Raises: Exception: If the API request fails or data processing encounters an error. """ endpoint = "/fapi/v1/fundingRate" # Construct query parameters params = { "symbol": symbol.upper(), # Ensure symbol is uppercase (e.g., BTCUSDT) } if startTime is not None: params["startTime"] = startTime if endTime is not None: params["endTime"] = endTime 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 funding_data: List[dict] = response.json() # Create pandas DataFrame df = pd.DataFrame(funding_data) # Convert fundingTime to readable format df["fundingTime"] = pd.to_datetime(df["fundingTime"], unit="ms") # Select relevant columns and format numbers df = df[["symbol", "fundingTime", "fundingRate"]] df["fundingRate"] = df["fundingRate"].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 Funding Rate data: {str(e)}")