get_top_chain_pairs_by_num_transfers
Identify the most active blockchain pairs by analyzing cross-chain transfer volumes on the Wormhole protocol to understand network usage patterns.
Instructions
Fetch top chain pairs by number of transfers from Wormholescan API.
Args:
timeSpan: Time span for data (7d, 15d, 30d). Default: 7d
Returns:
String representation of a pandas DataFrame containing top chain pairs by number of transfersInput Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| timeSpan | No | 7d |
Implementation Reference
- main.py:284-341 (handler)The handler function for the 'get_top_chain_pairs_by_num_transfers' tool, decorated with @mcp.tool() for registration. It validates the timeSpan parameter, fetches data from the Wormholescan API endpoint '/api/v1/top-chain-pairs-by-num-transfers', transforms the JSON response into a pandas DataFrame using the id2name helper for chain names, sorts by number of transfers, and returns a markdown representation of the table.
# Define the get_top_chain_pairs_by_num_transfers tool @mcp.tool() async def get_top_chain_pairs_by_num_transfers( timeSpan: str = "7d" ) -> str: """ Fetch top chain pairs by number of transfers from Wormholescan API. Args: timeSpan: Time span for data (7d, 15d, 30d). Default: 7d Returns: String representation of a pandas DataFrame containing top chain pairs by number of transfers """ try: # Validate parameters valid_time_spans = {"7d", "15d", "30d"} if timeSpan not in valid_time_spans: raise ValueError(f"Invalid timeSpan. Must be one of {valid_time_spans}") # Construct query parameters params = {"timeSpan": timeSpan} # Make API request async with httpx.AsyncClient() as client: response = await client.get( f"{API_BASE}/api/v1/top-chain-pairs-by-num-transfers", params=params ) response.raise_for_status() # Parse JSON response data = response.json() # Transform data for DataFrame rows = [ { "source_chain": id2name(item.get("emitterChain")), "destination_chain": id2name(item.get("destinationChain")), "number_of_transfers": item.get("numberOfTransfers") } for item in data.get("chainPairs", []) ] # Create DataFrame df = pd.DataFrame(rows) # Convert number_of_transfers to numeric df["number_of_transfers"] = pd.to_numeric(df["number_of_transfers"], errors="coerce") # Sort by number_of_transfers descending for readability df = df.sort_values("number_of_transfers", ascending=False) return df.to_markdown(index=False) except Exception as e: return str(e)