Skip to main content
Glama
kukapay

pumpfun-wallets-mcp

get_alpha_wallets

Identify top profitable wallets on Pumpfun and Pumpswap by analyzing trading activity and realized profits over the past 30 days using Dune Analytics data.

Instructions

Retrieve the top profitable wallets on Pumpfun and Pumpswap for the last 30 days.

This function queries Dune Analytics (query ID: 4032586) to fetch a ranked list of wallets
based on their realized profit over the past 30 days, formatted as a tabulated string.

Args:
    limit (int, optional): Maximum number of wallets to return. Defaults to 100.

Returns:
    str: A tabulated string containing the rank, wallet address, realized profit (in USD),
         and last transaction timestamp 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:65-92 (handler)
    The handler function for the 'get_alpha_wallets' tool. It uses @mcp.tool() decorator for registration and fetches top profitable wallets from Dune Analytics query ID 4032586, formatting the results as a tabulated string.
    @mcp.tool()
    def get_alpha_wallets(limit: int = 100) -> str:
        """
        Retrieve the top profitable wallets on Pumpfun and Pumpswap for the last 30 days.
    
        This function queries Dune Analytics (query ID: 4032586) to fetch a ranked list of wallets
        based on their realized profit over the past 30 days, formatted as a tabulated string.
    
        Args:
            limit (int, optional): Maximum number of wallets to return. Defaults to 100.
    
        Returns:
            str: A tabulated string containing the rank, wallet address, realized profit (in USD),
                 and last transaction timestamp 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(4032586, limit)
            rows = [
                [row["rank"], row["wallet"], f'${row["realized_profit"]:.0f}', row["last_tx"]]
                for row in data
            ]
            headers = ["Rank", "Wallet", "Realized Profit", "Last Tx"]
            return tabulate(rows, headers=headers)
        except:
            return ""
  • main.py:22-44 (helper)
    Helper function to fetch latest results from a specified Dune Analytics query, used by get_alpha_wallets and other tools.
    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 of behavioral disclosure. It effectively describes the tool's behavior: it queries Dune Analytics with a specific query ID, returns a tabulated string format, handles failures by returning an empty string, and can raise exceptions on errors. This covers key operational aspects without contradictions.

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 efficiently structured with a clear purpose statement followed by specific implementation details, parameter documentation, return value explanation, and error handling. Every sentence adds value without redundancy, and information is appropriately front-loaded.

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?

For a single-parameter tool with no annotations and no output schema, the description provides comprehensive context: purpose, data source, query specifics, return format, failure behavior, and error handling. The only minor gap is that it doesn't explicitly mention the tool is read-only (though this is implied by 'retrieve'), but overall it's highly complete.

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

Parameters5/5

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

The schema description coverage is 0%, so the description must fully compensate. It provides excellent parameter semantics: clearly explains the 'limit' parameter's purpose, optional nature, default value, and effect. This adds substantial meaning beyond what the bare schema provides.

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'), target resources ('top profitable wallets on Pumpfun and Pumpswap'), and timeframe ('last 30 days'). It distinguishes this from sibling tools like 'get_total_wallets' or 'get_trading_wallets' by focusing on profitability ranking rather than counts or distribution.

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

Usage Guidelines4/5

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

The description provides clear context about when to use this tool (to get ranked profitable wallets over 30 days), but doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools. The context is well-defined but lacks explicit exclusions or comparative guidance.

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

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/kukapay/pumpfun-wallets-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server