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kukapay

dex-metrics-mcp

get_monthly_trading_volume_by_dex

Retrieve monthly trading volume data for decentralized exchanges (DEXs) in a markdown-formatted pivot table with dates as index and DEX projects as columns.

Instructions

Retrieve monthly trading volume by decentralized exchange (DEX).

This tool fetches monthly 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:152-173 (handler)
    The main handler function decorated with @mcp.tool(), which registers and implements the tool logic. It fetches monthly trading volume data from Dune Analytics query ID 4323, processes it into a pivot table using pandas, sorts by date descending, and returns a markdown-formatted table or error message.
    @mcp.tool()
    def get_monthly_trading_volume_by_dex(limit: int = 1000) -> str:
        """
        Retrieve monthly trading volume by decentralized exchange (DEX).
    
        This tool fetches monthly 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(4323, 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 function used by the tool to fetch the latest results from a specified Dune Analytics query using the Dune 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 adds valuable behavioral context: it specifies the data source ('Dune Analytics query'), output format ('markdown-formatted pivot table'), structure ('dates as index, DEX projects as columns'), and error handling ('error message if query fails'). It doesn't mention 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 efficiently structured: first sentence states purpose, second adds behavioral details, and separate Args/Returns sections provide parameter and output specifics without redundancy. Every sentence adds value.

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 read tool with no annotations or output schema, the description is quite complete: it covers purpose, data source, output format, parameter semantics, and error handling. Minor gaps include lack of date range parameters or pagination details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description compensates by explaining the 'limit' parameter's purpose ('Maximum number of rows to retrieve'), default value (1000), and optional nature. However, it doesn't clarify what 'rows' refer to in the query context or provide range constraints.

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 monthly trading volume'), identifies the resource ('by decentralized exchange (DEX)'), and distinguishes it from siblings by specifying 'monthly' frequency and 'by DEX' grouping, unlike daily/weekly or chain/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 Guidelines4/5

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

The description implies usage context through 'monthly' and 'by DEX', helping differentiate from daily/weekly siblings or those focused on other entities like chains or aggregators. However, it lacks explicit when-not-to-use guidance or direct alternative naming.

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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