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get_recent_pumpfun_graduates

Track newly launched tokens on Pump.fun in the last 24 hours. Retrieve detailed data including token name, mint address, market cap, and trade count to analyze recent memecoin activity.

Instructions

Retrieve the most recently graduated tokens from Pump.fun in the last 24 hours.

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

Returns:
    str: A formatted table of recent Pump.fun graduates including graduation time,
        token name, mint address, market capitalization, and trade count,
        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:159-189 (handler)
    The handler function for the 'get_recent_pumpfun_graduates' tool. It fetches the latest results from Dune Analytics query ID 4832245, processes the rows by stripping HTML tags from token names and addresses, formats the data into a table using tabulate, and returns a markdown-formatted string with the results.
    @mcp.tool()
    def get_recent_pumpfun_graduates(limit: int = 100) -> str:
        """Retrieve the most recently graduated tokens from Pump.fun in the last 24 hours.
    
        Args:
            limit (int): Maximum number of tokens to return. Defaults to 100.
    
        Returns:
            str: A formatted table of recent Pump.fun graduates including graduation time,
                token name, mint address, market capitalization, and trade count,
                or an error message if the query fails.
    
        Raises:
            httpx.HTTPStatusError: If the Dune API request fails.
        """
        try:
            data = get_latest_result(4832245, limit=limit)
            rows = [
                [ 
                    row["graduation_time"], 
                    strip_a_tag(row["asset_with_chart"]), 
                    strip_a_tag(row["token_address_with_chart"]), 
                    f'${row["market_cap"]:.2f}', 
                    row["trade_count"] 
                ]
                for row in data
            ]
            headers = ["Graduation Time", "Token", "Mint Address", "Market Cap", "Trade Count"]
            return f"# Recent {limit} Pump.fun Graduates - 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 data from Dune API using the specified query ID and limit.
    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:47-50 (helper)
    Helper function used to extract text content from HTML anchor tags in the query results.
    def strip_a_tag(html):
        match = re.search(r'>(.*?)</a>', html)
        return match.group(1) if match else html
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 24-hour time window constraint, the formatted table output structure, error handling behavior, and specific API dependencies (Dune API). It doesn't mention rate limits or authentication requirements, but covers most essential behavioral aspects.

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?

Perfectly structured with purpose statement, args section, returns section, and raises section. Every sentence earns its place by providing essential information without redundancy. The information is front-loaded with the core functionality stated first.

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

Completeness5/5

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 complete context: purpose, parameter semantics, return format details, error conditions, and API dependencies. It addresses all necessary aspects given the tool's complexity and lack of structured annotations.

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, default value, and effect. It adds meaning beyond the bare schema by clarifying this controls the maximum number of tokens returned.

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 ('most recently graduated tokens from Pump.fun'), and temporal scope ('in the last 24 hours'). It distinguishes this tool from its siblings by focusing on recency rather than marketcap, volume, or trending metrics.

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 (for recent graduates within 24 hours), but doesn't explicitly state when not to use it or name specific alternatives among the sibling tools. The temporal scope helps differentiate from other graduate-related tools.

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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