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get_pumpfun_graduates_by_trading_volume

Retrieve and analyze Pump.fun tokens with the highest trading volume in the last 24 hours. Returns a formatted table with volume rank, token details, and graduation time for informed Solana memecoin tracking.

Instructions

Retrieve Pump.fun token launches sorted by highest trading volume in the last 24 hours.

Args:
    limit (int): Maximum number of tokens to return. Defaults to 100.

Returns:
    str: A formatted table of Pump.fun graduates including volume rank, token name,
        mint address, trading volume, and graduation time, or an error message if the query fails.

Raises:
    httpx.HTTPStatusError: If the Dune API request fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Implementation Reference

  • main.py:128-157 (handler)
    The main handler function for the tool, decorated with @mcp.tool(). Fetches data from Dune Analytics query 4832613, processes it using helper functions strip_a_tag and get_latest_result, formats a markdown table using tabulate, and returns the result as a string.
    @mcp.tool()
    def get_pumpfun_graduates_by_trading_volume(limit: int = 100) -> str:    
        """Retrieve Pump.fun token launches sorted by highest trading volume in the last 24 hours.
    
        Args:
            limit (int): Maximum number of tokens to return. Defaults to 100.
    
        Returns:
            str: A formatted table of Pump.fun graduates including volume rank, token name,
                mint address, trading volume, and graduation time, or an error message if the query fails.
    
        Raises:
            httpx.HTTPStatusError: If the Dune API request fails.
        """
        try:
            data = get_latest_result(4832613, limit=limit)
            rows = [
                [ 
                    row["volume_rank"], 
                    strip_a_tag(row["asset_with_chart"]), 
                    strip_a_tag(row["token_address_with_chart"]), 
                    f'${row["total_volume"]:.2f}', 
                    row["graduation_time"] 
                ]
                for row in data
            ]
            headers = ["Rank", "Token", "Mint Address", "Volume(12h)", "Graduation Time"]
            return f"# Top {limit} Pump.fun Graduates by Trading Volume - Last 24 Hours\n\n" + tabulate(rows, headers=headers)
        except Exception as e:
            return str(e)
  • main.py:23-46 (helper)
    Helper function used by the tool to fetch the latest execution results from a specified Dune Analytics query ID via the Dune API.
    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
  • main.py:47-49 (helper)
    Helper function used by the tool to extract plain text from HTML anchor tags (e.g., token names and addresses).
    def strip_a_tag(html):
        match = re.search(r'>(.*?)</a>', html)
        return match.group(1) if match else html
  • main.py:128-128 (registration)
    The @mcp.tool() decorator registers the function as an MCP tool.
    @mcp.tool()
  • Type hints and docstring defining the input schema (limit: int = 100) and output (str).
    def get_pumpfun_graduates_by_trading_volume(limit: int = 100) -> str:    
        """Retrieve Pump.fun token launches sorted by highest trading volume in the last 24 hours.
    
        Args:
            limit (int): Maximum number of tokens to return. Defaults to 100.
    
        Returns:
            str: A formatted table of Pump.fun graduates including volume rank, token name,
                mint address, trading volume, and graduation time, or an error message if the query fails.
    
        Raises:
            httpx.HTTPStatusError: If the Dune API request fails.
        """
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: the data source (Dune API), error behavior (returns error message on query failure), specific exception type (httpx.HTTPStatusError), and return format (formatted table with specific columns). It doesn't mention rate limits or authentication needs, but provides substantial behavioral context.

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 organized sections for Args, Returns, and Raises. Every sentence serves a distinct purpose with zero wasted words, making it 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?

For a single-parameter read operation with no output schema, the description provides excellent context: clear purpose, parameter explanation, return format details, and error handling. The only minor gap is lack of explicit sibling tool differentiation, but overall it's highly complete for its complexity level.

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% schema description coverage for the single parameter, the description fully compensates by explaining the 'limit' parameter's purpose ('Maximum number of tokens to return') and default value (100). This adds complete meaning beyond the bare 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'), resource ('Pump.fun token launches'), and key sorting criteria ('sorted by highest trading volume in the last 24 hours'). It distinguishes itself from siblings like 'get_pumpfun_graduates_by_marketcap' by specifying the volume-based sorting approach.

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 the sorting criteria (24-hour trading volume), but doesn't explicitly state when to use this tool versus alternatives like 'get_pumpfun_graduates_by_marketcap' or 'get_recent_pumpfun_graduates'. No explicit exclusions or prerequisites are provided.

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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