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kukapay

pumpfun-wallets-mcp

get_trading_wallets

Retrieve top wallets by all-time trading volume on Pumpfun and Pumpswap. Query Dune Analytics to fetch a ranked list of wallets, displaying rank, address, trade count, and total trading volume in USD.

Instructions

Retrieve the top wallets by all-time trading volume on Pumpfun and Pumpswap.

This function queries Dune Analytics (query ID: 5232018) to fetch a ranked list of wallets
based on their total trading volume, formatted as a tabulated string.

Args:
    limit (int, optional): Maximum number of wallets to return. Defaults to 10.

Returns:
    str: A tabulated string containing the rank, wallet address, trade count, and total
         trading volume (in USD) for each wallet, or an empty string if the query fails.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Implementation Reference

  • main.py:94-121 (handler)
    The main handler function for the get_trading_wallets tool. It is decorated with @mcp.tool() for registration. Fetches data from Dune Analytics query ID 5232018 using the helper get_latest_result, processes it into a tabulated string of top trading wallets by volume.
    @mcp.tool()
    def get_trading_wallets(limit: int = 10) -> str:
        """
        Retrieve the top wallets by all-time trading volume on Pumpfun and Pumpswap.
    
        This function queries Dune Analytics (query ID: 5232018) to fetch a ranked list of wallets
        based on their total trading volume, formatted as a tabulated string.
    
        Args:
            limit (int, optional): Maximum number of wallets to return. Defaults to 10.
    
        Returns:
            str: A tabulated string containing the rank, wallet address, trade count, and total
                 trading volume (in USD) for each wallet, or an empty string if the query fails.
    
        Raises:
            Exception: If the API request or data retrieval encounters an error.
        """
        try:
            data = get_latest_result(5232018, limit)
            rows = [
                [row["rank"], row["wallet"], row["trade_count"], f'${row["total_volume_usd"]:.0f}']
                for row in data
            ]
            headers = ["Rank", "Wallet", "Trade Count", "Total Volume"]
            return tabulate(rows, headers=headers)
        except:
            return ""
  • main.py:22-45 (helper)
    Helper function used by get_trading_wallets (and other tools) to fetch latest results from a specified Dune Analytics query.
    def get_latest_result(query_id: int, limit: int = 1000):
        """
        Fetch the latest results from a Dune Analytics query.
    
        Args:
            query_id (int): The ID of the Dune query to fetch results from.
            limit (int, optional): Maximum number of rows to return. Defaults to 1000.
    
        Returns:
            list: A list of dictionaries containing the query results, or an empty list if the request fails.
    
        Raises:
            httpx.HTTPStatusError: If the API request fails due to a client or server error.
        """
        url = f"{BASE_URL}/query/{query_id}/results"
        params = {"limit": limit}
        with httpx.Client() as client:
            response = client.get(url, params=params, headers=HEADERS, timeout=300)
            response.raise_for_status()
            data = response.json()
            
        result_data = data.get("result", {}).get("rows", [])
        return result_data
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and does well by disclosing key behaviors: it queries Dune Analytics (query ID specified), returns a tabulated string, handles failures with an empty string, and raises exceptions on errors. It covers data source, output format, and error handling, though it could mention rate limits or permissions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded with the core purpose, followed by details on args, returns, and raises. Every sentence adds value without redundancy, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is largely complete: it explains purpose, parameters, return format, and error behavior. It could be slightly enhanced by mentioning data freshness or query limitations, but it covers essential aspects adequately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds significant meaning beyond the input schema, which has 0% coverage. It explains the 'limit' parameter's purpose (maximum number of wallets), default value (10), and effect on the output, compensating fully for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Retrieve'), resource ('top wallets by all-time trading volume'), and scope ('on Pumpfun and Pumpswap'), distinguishing it from sibling tools like get_alpha_wallets or get_total_wallets by focusing on trading volume ranking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for fetching ranked trading volume data but does not explicitly state when to use this tool versus alternatives like get_trading_wallet_distribution or other siblings. It provides context (ranking by volume) but lacks explicit guidance on exclusions or comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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