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kukapay

dex-metrics-mcp

get_latest_trading_volume_by_dex

Retrieve 24-hour and 7-day trading volume data for decentralized exchanges to analyze market activity and DEX performance.

Instructions

Retrieve the latest 24-hour and 7-day trading volume by decentralized exchange (DEX).

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:45-63 (handler)
    Handler function decorated with @mcp.tool() that implements the tool logic. Fetches data from Dune query 4319 using the helper get_latest_result, processes into a pandas DataFrame indexed by Rank, sorted ascending, and returns as markdown table or error string.
    @mcp.tool()
    def get_latest_trading_volume_by_dex(limit: int = 1000) -> str:
        """
        Retrieve the latest 24-hour and 7-day trading volume by decentralized exchange (DEX).
    
        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(4319, limit=limit)
            df = pd.DataFrame(data)
            df = df.set_index("Rank")
            df = df.sort_index(ascending=True)
            return df.to_markdown()
        except Exception as e:
            return str(e)
  • main.py:21-43 (helper)
    Supporting utility function used by the tool (and others) to retrieve latest execution results from a specified Dune Analytics query ID via API, returning list of rows.
    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 specifies the return format ('markdown-formatted table') and error handling ('error message if the query fails'), which adds useful context. However, it lacks details on rate limits, authentication needs, or data freshness, which are important for a data retrieval tool.

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 appropriately sized and front-loaded, with the core purpose stated first, followed by clear sections for arguments and returns. Every sentence adds value without redundancy, 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 the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is fairly complete. It covers purpose, parameter semantics, and return format. However, it could improve by addressing behavioral aspects like data latency or usage compared to siblings, which would enhance completeness for an agent.

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 meaning beyond the input schema by explaining that the 'limit' parameter controls the 'maximum number of rows to retrieve from the query' and specifies a default value of 1000. With schema description coverage at 0%, this compensates well for the single parameter, though it could clarify what 'rows' represent (e.g., DEX entries).

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'), resource ('latest 24-hour and 7-day trading volume'), and scope ('by decentralized exchange (DEX)'). It distinguishes itself from siblings like 'get_daily_trading_volume_by_dex' by specifying the timeframes (24-hour and 7-day) rather than daily, and from 'get_latest_trading_volume_by_aggregator' by focusing on DEX rather than aggregator.

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 retrieving recent trading volume data by DEX, but does not explicitly state when to use this tool versus alternatives like 'get_daily_trading_volume_by_dex' or 'get_weekly_trading_volume_by_dex'. No exclusions or prerequisites are mentioned, leaving the context somewhat open-ended.

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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