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kukapay

pumpfun-wallets-mcp

get_trading_wallet_distribution

Analyze wallet distribution by trading volume on Pumpfun and Pumpswap, excluding bots. Provides a clear breakdown of volume tiers and wallet counts using data from Dune Analytics.

Instructions

Retrieve the distribution of wallets by trading volume on Pumpfun and Pumpswap, excluding bots.

This function queries Dune Analytics (query ID: 5239138) to fetch the number of wallets
grouped by trading volume tiers, formatted as a tabulated string.

Returns:
    str: A tabulated string containing the volume tier and number of wallets in each tier,
         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

No arguments

Implementation Reference

  • main.py:123-148 (handler)
    The handler function for the 'get_trading_wallet_distribution' tool. It is decorated with @mcp.tool() for registration and fetches the wallet distribution data from Dune Analytics query ID 5239138, processes it into a tabulated string using tabulate, or returns empty string on error.
    @mcp.tool()
    def get_trading_wallet_distribution() -> str:
        """
        Retrieve the distribution of wallets by trading volume on Pumpfun and Pumpswap, excluding bots.
    
        This function queries Dune Analytics (query ID: 5239138) to fetch the number of wallets
        grouped by trading volume tiers, formatted as a tabulated string.
    
        Returns:
            str: A tabulated string containing the volume tier and number of wallets in each tier,
                 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(5239138)
            rows = [
                [row["volume_tier"], row["num_wallets"]]
                for row in data
            ]
            headers = ["Volume Tier", "Number of Wallets"]
            return tabulate(rows, headers=headers)
        except:
            return ""
  • main.py:22-45 (helper)
    Supporting helper function used by the tool to fetch the latest execution 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 full burden and does well by disclosing key behavioral traits: data source (Dune Analytics), query failure behavior (returns empty string), error handling (raises Exception), and output format (tabulated string). It doesn't mention rate limits or authentication needs, but covers most essential operational aspects.

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

Conciseness4/5

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

The description is well-structured with clear sections (purpose, data source, returns, raises) and efficiently conveys necessary information in 5 sentences. It could be slightly more concise by combining some sentences, but overall wastes no space.

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 parameterless tool with no annotations and no output schema, the description provides comprehensive context: purpose, data source, exclusion criteria, return format, and error behavior. The only minor gap is not explicitly stating the volume tier ranges, but given the query ID reference, this is reasonable.

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?

With 0 parameters and 100% schema coverage, the baseline is 4. The description appropriately explains this is a parameterless function that retrieves pre-defined data, which aligns perfectly with the empty input schema.

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 the distribution of wallets'), resource ('wallets by trading volume on Pumpfun and Pumpswap'), and scope ('excluding bots'). It distinguishes from sibling tools like 'get_total_wallets' by specifying trading volume distribution rather than total counts.

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 context through 'excluding bots' and mentions data source (Dune Analytics query ID: 5239138), but doesn't explicitly state when to use this tool versus alternatives like 'get_trading_wallets' or provide clear exclusions. The guidance is contextual but not explicit about alternatives.

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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