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get_daily_sales_by_collection

Retrieve daily sales counts for NFT collections to analyze trading activity trends over time.

Instructions

Retrieve number of daily sales for NFT collections.

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

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Implementation Reference

  • main.py:66-85 (handler)
    The handler function for the 'get_daily_sales_by_collection' tool. It fetches data from Dune Analytics query ID 5140487, processes it into a pivoted DataFrame by day and collection, and returns a markdown-formatted table of daily sales counts.
    @mcp.tool()
    def get_daily_sales_by_collection(limit: int = 1000) -> str:
        """
        Retrieve number of daily sales for NFT collections.
    
        Args:
            limit (int, optional): Maximum number of rows to fetch from the query. Defaults to 1000.
    
        Returns:
            str: Markdown table of daily sales counts by collection, or error message if the query fails.
        """
        try:
            data = get_latest_result(5140487, limit=limit)
            df = pd.DataFrame(data)
            df["day"] = pd.to_datetime(df["day"]).dt.date
            pivot_df = df.pivot(index="day", columns="collection", values="sales_count")
            pivot_df = pivot_df.sort_index(ascending=False)
            return pivot_df.to_markdown()
        except Exception as e:
            return str(e)  
  • main.py:21-44 (helper)
    Supporting helper function that fetches the latest execution results from a specified Dune Analytics query ID using the Dune API, which is called by the tool handler.
    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 adequately describes the core functionality (retrieving sales counts) and output format (Markdown table or error message), but lacks details about performance characteristics, rate limits, authentication requirements, or what constitutes a 'failed' query beyond error messages.

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 dedicated Args and Returns sections. Every sentence adds value without redundancy, making it easy to scan and understand quickly.

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

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (single optional parameter, no output schema, no annotations), the description covers the basics adequately but has gaps. It explains what the tool does and the parameter, but doesn't address sibling differentiation, error conditions in detail, or data freshness, which would help an agent use it more effectively.

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' by explaining it controls 'Maximum number of rows to fetch from the query' and provides the default value. Since schema description coverage is 0% and there's only one parameter, this compensates well, though it could specify units or constraints like minimum/maximum values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Retrieve') and resource ('number of daily sales for NFT collections'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from its siblings like 'get_daily_trading_volume_by_collection' or 'get_average_price_by_collection', which would require more specific language about what distinguishes sales counts from other metrics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus its siblings. It doesn't mention alternatives, prerequisites, or specific contexts where this tool is preferred over others like 'get_daily_trading_volume_by_collection' for volume data or 'get_average_price_by_collection' for price metrics.

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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