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kukapay

pumpfun-wallets-mcp

get_total_wallets

Retrieve the total number of wallets on Pumpfun and Pumpswap by querying Dune Analytics. Returns the wallet count as an integer or 0 if the query fails. Handles API errors during data retrieval.

Instructions

Retrieve the total number of wallets on Pumpfun and Pumpswap platforms.

This function queries Dune Analytics (query ID: 5239155) to fetch the total wallet count.

Returns:
    int: The total number of wallets, or 0 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:46-63 (handler)
    The main handler function for the 'get_total_wallets' tool. It is decorated with @mcp.tool() for registration and implements the core logic by calling the helper get_latest_result on Dune query 5239155 to retrieve and return the total number of wallets, defaulting to 0 on error.
    @mcp.tool()
    def get_total_wallets() -> int:
        """
        Retrieve the total number of wallets on Pumpfun and Pumpswap platforms.
    
        This function queries Dune Analytics (query ID: 5239155) to fetch the total wallet count.
    
        Returns:
            int: The total number of wallets, or 0 if the query fails.
    
        Raises:
            Exception: If the API request or data retrieval encounters an error.
        """
        try:
            data = get_latest_result(5239155)
            return data[0].get("total_wallets", 0)
        except:
            return 0
  • main.py:22-44 (helper)
    Supporting helper function used by get_total_wallets (and others) 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: it describes the data source (Dune Analytics query), failure behavior (returns 0 if query fails), and error handling (raises Exception on API/data retrieval errors). It doesn't mention rate limits, caching, or authentication needs, but covers core operational aspects adequately.

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 perfectly structured and concise: first sentence states purpose, second explains implementation, followed by clear 'Returns' and 'Raises' sections. Every sentence adds value with zero waste, and information is front-loaded appropriately for a zero-parameter tool.

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 simplicity (0 parameters, no output schema, no annotations), the description is nearly complete: it explains what it does, how it works, what it returns, and error behavior. The only minor gap is lack of sibling differentiation, but for a straightforward query tool, this provides sufficient context for an agent to use it correctly.

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 tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description correctly indicates no parameters are needed ('This function queries...'), adding no unnecessary parameter information while matching the schema's empty state.

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 ('total number of wallets'), and scope ('on Pumpfun and Pumpswap platforms'), distinguishing it from sibling tools like get_alpha_wallets or get_trading_wallets which focus on different wallet subsets. It provides a complete purpose statement without being vague or tautological.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus its siblings (get_alpha_wallets, get_trading_wallet_distribution, get_trading_wallets). It mentions the data source (Dune Analytics query ID: 5239155) but doesn't specify use cases, prerequisites, or alternatives, leaving the agent with no contextual decision-making help.

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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