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get_pumpfun_graduates_by_marketcap

Retrieve the top Pump.fun token launches ranked by market capitalization within the last 24 hours. Provides a formatted table including rank, token name, mint address, market cap, and trade count for analysis.

Instructions

Retrieve Pump.fun token launches sorted by highest market capitalization 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 rank, 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:97-126 (handler)
    The @mcp.tool()-decorated handler function that implements the 'get_pumpfun_graduates_by_marketcap' tool. It fetches data from Dune Analytics query ID 4124453, processes rows using helper functions like strip_a_tag, formats a table with tabulate, and returns a markdown-formatted string or error message.
    @mcp.tool()
    def get_pumpfun_graduates_by_marketcap(limit: int = 100) -> str:
        """Retrieve Pump.fun token launches sorted by highest market capitalization 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 rank, 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(4124453, limit=limit)
            rows = [
                [ 
                    row["rank"], 
                    strip_a_tag(row["asset_with_chart"]), 
                    row["token_address"], 
                    f'${row["market_cap"]:.2f}', 
                    row["trade_count"] 
                ]
                for row in data
            ]
            headers = ["Rank", "Token", "Mint Address", "MarketCap", "Trade Count"]
            return f"# Top {limit} Pump.fun Graduates by MarketCap - Last 24 Hours\n\n" + tabulate(rows, headers=headers)
        except Exception as e:
            return str(e)
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 retrieves data from Dune API, returns a formatted table or error message, and can raise HTTP errors. It covers key aspects like data source, output format, and error handling, though it lacks details on rate limits or authentication needs.

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 well-structured and front-loaded with the core purpose, followed by organized sections for Args, Returns, and Raises. Every sentence adds value without redundancy, making it efficient and easy for an agent 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?

Given the tool's moderate complexity (one parameter, no output schema, no annotations), the description is largely complete. It explains the purpose, parameter, return format, and error handling. However, it could improve by mentioning data freshness or limitations, such as the exact timing of the 24-hour window or potential API constraints.

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 schema description coverage is 0%, so the description must compensate. It clearly explains the 'limit' parameter's purpose ('Maximum number of tokens to return') and default value, adding meaningful semantics beyond the basic schema. Since there is only one parameter, this is sufficient for effective use.

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 market capitalization in the last 24 hours'). It distinguishes from sibling tools like 'get_pumpfun_graduates_by_trading_volume' by specifying the market cap focus, making the purpose unambiguous.

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 implies usage context by specifying 'in the last 24 hours' and sorting by market cap, which helps differentiate it from siblings like 'get_recent_pumpfun_graduates' (likely unsorted or differently sorted). However, it does not explicitly state when to use this tool versus alternatives or mention any exclusions, leaving some ambiguity for the agent.

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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