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kukapay

dex-metrics-mcp

get_daily_trading_volume_by_dex

Retrieve daily trading volume data for decentralized exchanges (DEXs) from Dune Analytics, presented as a markdown-formatted pivot table with dates and DEX projects.

Instructions

Retrieve daily trading volume by decentralized exchange (DEX).

This tool fetches daily trading volume data for various DEXs from a Dune Analytics query and returns it in a markdown-formatted pivot table, with dates as the index and DEX projects as columns.

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

Returns:
    str: A markdown-formatted pivot table of trading volume data, or an error message if the query fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Implementation Reference

  • main.py:107-127 (handler)
    The handler function implementing the get_daily_trading_volume_by_dex tool. It uses the @mcp.tool() decorator for registration, fetches data from Dune Analytics query ID 4388 via the helper get_latest_result, processes it with pandas into a pivot table, and returns a markdown table.
    def get_daily_trading_volume_by_dex(limit: int = 1000) -> str:
        """
        Retrieve daily trading volume by decentralized exchange (DEX).
    
        This tool fetches daily trading volume data for various DEXs from a Dune Analytics query and returns it in a markdown-formatted pivot table, with dates as the index and DEX projects as columns.
    
        Args:
            limit (int, optional): Maximum number of rows to retrieve from the query. Defaults to 1000.
    
        Returns:
            str: A markdown-formatted pivot table of trading volume data, or an error message if the query fails.
        """
        try:
            data = get_latest_result(4388, limit=limit)
            df = pd.DataFrame(data)
            df["date"] = pd.to_datetime(df["_col1"]).dt.date
            pivot_df = df.pivot(index="date", columns="project", values="usd_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 utility function used by the tool to fetch the latest execution results from a specified Dune Analytics query via their API.
    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
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 key behaviors: it fetches from Dune Analytics, returns markdown-formatted pivot tables with specific structure (dates as index, DEX projects as columns), and may return error messages on query failure. It doesn't mention rate limits, authentication needs, or data freshness, but covers core operational traits.

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, operational details, and separate Args/Returns sections. Every sentence adds value: first states what it does, second explains source and output format, third documents the parameter, fourth describes return value. No wasted words.

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?

For a single-parameter query tool with no annotations or output schema, the description is quite complete: it explains purpose, data source, output format, parameter semantics, and error behavior. It could mention query execution time or data latency, but covers essential context given the tool's moderate complexity.

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: it explains 'limit' controls 'maximum number of rows to retrieve from the query' with a default of 1000. Since schema description coverage is 0% (schema only has title 'Limit'), this compensates well by providing purpose and default value 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 specific action ('retrieve daily trading volume'), resource ('by decentralized exchange'), and data source ('from a Dune Analytics query'). It distinguishes from siblings by specifying 'daily' frequency and 'by DEX' focus, unlike weekly/monthly or by chain/aggregator/frontend alternatives.

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 context through 'daily trading volume' and 'DEX' focus, suggesting when this tool is appropriate. However, it doesn't explicitly state when to use this vs. siblings like get_weekly_trading_volume_by_dex or get_latest_trading_volume_by_dex, nor does it mention prerequisites or exclusions.

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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