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get_daily_trading_volume_by_collection

Retrieve daily trading volume data for top Ethereum NFT collections to analyze market activity and trends.

Instructions

Retrieve daily trading volume for top 5 Ethereum NFT collections.

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

Returns:
    str: Markdown table of daily trading volumes by collection, or error message if the query fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Implementation Reference

  • main.py:46-64 (handler)
    Handler function decorated with @mcp.tool(). Fetches data from Dune query ID 5140422 using the get_latest_result helper, processes it into a pivot table of daily trading volumes by collection using pandas, sorts by date descending, and returns a markdown table or error string.
    def get_daily_trading_volume_by_collection(limit: int = 1000) -> str:
        """
        Retrieve daily trading volume for top 5 Ethereum NFT collections.
    
        Args:
            limit (int, optional): Maximum number of rows to fetch from the query. Defaults to 1000.
    
        Returns:
            str: Markdown table of daily trading volumes by collection, or error message if the query fails.
        """
        try:
            data = get_latest_result(5140422, limit=limit)
            df = pd.DataFrame(data)
            df["day"] = pd.to_datetime(df["day"]).dt.date
            pivot_df = df.pivot(index="day", columns="collection", values="daily_volume")
            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)
    Helper function to fetch the latest execution results from a Dune Analytics query API using httpx, returning the rows as 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
  • main.py:46-46 (registration)
    The tool is registered using the @mcp.tool() decorator on the handler function.
    def get_daily_trading_volume_by_collection(limit: int = 1000) -> str:
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 mentions the return format ('Markdown table') and error handling ('error message if the query fails'), which adds useful context beyond basic functionality. However, it lacks details on rate limits, data freshness, or authentication needs, leaving gaps for a tool with no annotation support.

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

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 (1 parameter, no output schema, no annotations), the description is largely complete. It covers purpose, parameter semantics, and return format. However, it lacks explicit guidance on when to use versus siblings and omits behavioral details like data sources or update frequency, which could enhance completeness.

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 semantics for the single parameter 'limit', explaining it as 'Maximum number of rows to fetch from the query' with a default of 1000. Since schema description coverage is 0%, this compensates well by clarifying the parameter's purpose and default behavior, though it doesn't specify constraints like minimum/maximum values.

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 daily trading volume') and resource ('top 5 Ethereum NFT collections'), distinguishing it from siblings like get_average_price_by_collection or get_daily_sales_by_collection. It precisely defines the scope and target data.

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 fetching daily trading volume data, but does not explicitly state when to use this tool versus alternatives like get_daily_sales_by_collection or get_average_price_by_collection. No exclusions or prerequisites are mentioned, leaving usage context inferred rather than defined.

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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