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kukapay

dex-metrics-mcp

get_weekly_trading_volume_by_frontend

Retrieve 7-day trading volume data for different frontends from Dune Analytics. Returns a markdown-formatted table sorted by volume to analyze platform performance.

Instructions

Retrieve 7-day trading volume by frontend.

This tool fetches 7-day trading volume data for different frontends from a Dune Analytics query and returns it in a markdown-formatted table, sorted by volume in descending order.

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

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Implementation Reference

  • main.py:221-239 (handler)
    The main handler function for the 'get_weekly_trading_volume_by_frontend' tool. It fetches the latest results from Dune query ID 3364122, processes the data into a pandas DataFrame, sorts it by 'volume' in descending order, and returns a markdown-formatted table. Includes error handling.
    def get_weekly_trading_volume_by_frontend(limit: int = 1000) -> str:
        """
        Retrieve 7-day trading volume by frontend.
    
        This tool fetches 7-day trading volume data for different frontends from a Dune Analytics query and returns it in a markdown-formatted table, sorted by volume in descending order.
    
        Args:
            limit (int, optional): Maximum number of rows to retrieve from the query. Defaults to 1000.
    
        Returns:
            str: A markdown-formatted table of trading volume data, or an error message if the query fails.
        """
        try:
            data = get_latest_result(3364122, limit=limit)
            df = pd.DataFrame(data)
            df = df.sort_values(by="volume", ascending=False)
            return df.to_markdown()
        except Exception as e:
            return str(e)  
  • main.py:220-220 (registration)
    The @mcp.tool() decorator registers the get_weekly_trading_volume_by_frontend function as an MCP tool.
    @mcp.tool()
  • main.py:21-43 (helper)
    Shared helper function used by the tool to fetch the latest execution results from a specified Dune Analytics query ID, returning the rows as a list of dictionaries.
    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 table), sorting behavior (descending by volume), and error handling (returns error message on query failure). It does not mention rate limits or authentication needs, but covers key operational aspects.

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 front-loaded: the first sentence states the core purpose, followed by elaboration on behavior, parameters, and returns. Each sentence adds distinct value with zero waste, making it efficient and easy to parse.

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 no annotations and no output schema, the description provides good coverage: it explains the tool's purpose, behavior, parameter, and return format. It could be more complete by specifying the exact columns in the output table or query details, but for a single-parameter query tool, it is largely sufficient.

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?

Schema description coverage is 0%, so the description must compensate. It documents the single parameter 'limit', explaining its purpose ('Maximum number of rows to retrieve'), optional nature, and default value (1000). This fully covers the parameter semantics beyond the basic schema, though it could note that the limit applies to query rows before sorting.

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', 'fetches'), resource ('7-day trading volume data for different frontends'), and source ('from a Dune Analytics query'). It distinguishes from siblings by specifying 'by frontend' rather than by dex, chain, or other dimensions present in sibling tool names.

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 '7-day trading volume' and 'by frontend', suggesting this is for weekly frontend-level analysis. However, it does not explicitly state when to use this tool versus alternatives like daily or monthly tools, or tools focusing on other dimensions like dex or chain, leaving some ambiguity.

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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