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kukapay

solana-launchpads-mcp

get_daily_graduation_rate

Fetch daily graduation rates for Solana memecoin launchpads to track project success percentages by platform.

Instructions

Fetch the daily graduation rate of Solana memecoin launchpads.

This tool retrieves data from a Dune Analytics query and pivots it to show the graduation rate
(e.g., percentage of projects that successfully complete their goals) per day by each platform.

Args:
    limit (int, optional): Maximum number of rows to fetch from the Dune query. Defaults to 1000.

Returns:
    str: A markdown-formatted table of daily graduation rates by platform, or an error message
        if the query fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Implementation Reference

  • main.py:100-123 (handler)
    The handler function for the get_daily_graduation_rate tool. It is decorated with @mcp.tool() for registration. Fetches data from Dune query ID 5129526 using the shared get_latest_result helper, processes it into a pivot table showing graduation rates by platform and date using pandas, sorts by date descending, and returns a markdown-formatted table. Catches exceptions and returns error string.
    @mcp.tool()
    def get_daily_graduation_rate(limit: int = 1000) -> str:
        """
        Fetch the daily graduation rate of Solana memecoin launchpads.
    
        This tool retrieves data from a Dune Analytics query and pivots it to show the graduation rate
        (e.g., percentage of projects that successfully complete their goals) per day by each platform.
    
        Args:
            limit (int, optional): Maximum number of rows to fetch from the Dune query. Defaults to 1000.
    
        Returns:
            str: A markdown-formatted table of daily graduation rates by platform, or an error message
                if the query fails.
        """
        try:
            data = get_latest_result(5129526, limit=limit)
            df = pd.DataFrame(data)
            df['block_date'] = pd.to_datetime(df['block_date']).dt.date
            pivot_df = df.pivot(index="block_date", columns="platform", values="graduation_rate")
            pivot_df = pivot_df.sort_index(ascending=False)
            return pivot_df.to_markdown()
        except Exception as e:
            return str(e)
  • main.py:21-43 (helper)
    Shared utility function used by get_daily_graduation_rate (and other tools) to fetch the latest execution results from a specified Dune Analytics query ID via the Dune API, returning the rows as a list of dicts.
    def get_latest_result(query_id: int, limit: int = 1000) -> list:
        """
        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
Behavior3/5

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 describes the data source (Dune Analytics query), transformation (pivoting to show graduation rate per day by platform), and output format (markdown table or error message), which covers key behavioral aspects. However, it doesn't mention rate limits, authentication needs, or error handling specifics beyond a generic mention of query failure.

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 concise, with a clear purpose statement followed by specific details about data source, transformation, parameters, and return values. Every sentence adds value without redundancy, and it's appropriately front-loaded with the core functionality.

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 optional parameter, no output schema, no annotations), the description provides good completeness. It explains what the tool does, the parameter's purpose, and the return format. However, it could be more complete by addressing potential limitations or edge cases, such as what happens if the Dune query times out or how historical data availability is handled.

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 description adds meaningful context for the single parameter 'limit', explaining it controls 'maximum number of rows to fetch from the Dune query' with a default of 1000. Since schema description coverage is 0% (the schema only lists 'limit' as an integer with default 1000 without semantic explanation), the description compensates well by providing clear parameter semantics 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 tool's purpose with specific verbs ('fetch', 'retrieves') and resources ('daily graduation rate of Solana memecoin launchpads'), distinguishing it from sibling tools like get_daily_active_addresses or get_daily_tokens_deployed by focusing on graduation rates rather than addresses or token deployments.

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 for retrieving graduation rate data from Dune Analytics, but does not explicitly state when to use this tool versus alternatives or provide context on prerequisites. It mentions the data source and transformation, which gives some implied guidance, but lacks explicit when/when-not instructions.

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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