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kukapay

dex-metrics-mcp

get_weekly_trading_volume_by_chain

Retrieve weekly trading volume data segmented by blockchain to analyze DEX performance trends across different networks.

Instructions

Retrieve weekly trading volume by blockchain.

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:85-104 (handler)
    The handler function for the 'get_weekly_trading_volume_by_chain' tool, registered via @mcp.tool() decorator. It fetches data from Dune Analytics query ID 2180075, processes it into a pivot table (date by blockchain, usd_volume), sorts by date descending, and returns a markdown table. Includes input validation via type hints and docstring, error handling.
    @mcp.tool()
    def get_weekly_trading_volume_by_chain(limit: int = 1000) -> str:
        """
        Retrieve weekly trading volume by blockchain.
    
        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(2180075, limit=limit)
            df = pd.DataFrame(data)
            df["date"] = pd.to_datetime(df["_col1"]).dt.date
            pivot_df = df.pivot(index="date", columns="blockchain", 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)
    Supporting helper function used by the tool to retrieve latest execution results from a specified Dune Analytics query via the Dune API, handling HTTP requests and parsing JSON response.
    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
  • main.py:85-85 (registration)
    The @mcp.tool() decorator registers the get_weekly_trading_volume_by_chain function as an MCP tool in the FastMCP server.
    @mcp.tool()
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 adds some context beyond the schema by specifying the return format ('markdown-formatted pivot table') and error handling ('error message if the query fails'), but lacks details on rate limits, authentication needs, data freshness, or other 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 well-structured and front-loaded, with the core purpose stated first, followed by clear sections for Args 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.

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 (one optional parameter, no output schema), the description is partially complete. It covers the purpose, parameter, and return format, but lacks context on data sources, time ranges, or how it differs from siblings, leaving gaps for effective agent use.

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 semantics for the single parameter 'limit', explaining it as 'Maximum number of rows to retrieve from the query' with a default value, which compensates for the 0% schema description coverage. Since there's only one parameter, this provides adequate clarity, though it doesn't elaborate on row content or ordering.

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 tool's purpose with 'Retrieve weekly trading volume by blockchain', specifying both the action (retrieve) and resource (weekly trading volume by blockchain). It distinguishes itself from siblings by focusing on weekly data aggregated by chain rather than by DEX, aggregator, or other dimensions, though it doesn't explicitly contrast with them.

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 alternatives. With multiple sibling tools for trading volume data (e.g., get_daily_trading_volume_by_dex, get_monthly_trading_volume_by_dex), there's no indication of whether this tool is preferred for weekly summaries, chain-level analysis, or other contexts, leaving usage unclear.

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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