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kukapay

dex-metrics-mcp

get_weekly_solana_trading_volume_by_dex

Retrieve weekly Solana DEX trading volume data formatted as a markdown pivot table for analysis.

Instructions

Retrieve weekly trading volume by decentralized exchange (DEX) on the Solana blockchain.

This tool fetches weekly trading volume data for DEXs on Solana 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:197-218 (handler)
    The handler function for the tool 'get_weekly_solana_trading_volume_by_dex', decorated with @mcp.tool() for registration. It fetches data from Dune Analytics query 3084516, processes it into a pivot table using pandas, and returns a markdown-formatted string.
    @mcp.tool()
    def get_weekly_solana_trading_volume_by_dex(limit: int = 1000) -> str:
        """
        Retrieve weekly trading volume by decentralized exchange (DEX) on the Solana blockchain.
    
        This tool fetches weekly trading volume data for DEXs on Solana 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(3084516, limit=limit)
            df = pd.DataFrame(data)
            df["date"] = pd.to_datetime(df["time"]).dt.date
            pivot_df = df.pivot(index="date", columns="project", values="amount_usd")
            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 latest results from a specified Dune Analytics query ID, handling HTTP requests and returning the rows as a list of dicts.
    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 the full burden of behavioral disclosure. It effectively describes key behaviors: it fetches data from Dune Analytics, returns a markdown-formatted pivot table with dates as index and DEX projects as columns, and may return an error message on query failure. However, it does not cover aspects like rate limits, authentication needs, or data freshness, leaving some gaps.

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, starting with the core purpose, followed by details on data source and output format, and ending with parameter and return value explanations. Each 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 largely complete: it covers purpose, data source, output format, parameter semantics, and error handling. However, it lacks details on behavioral aspects like rate limits or data update frequency, which would enhance completeness for a data-fetching tool.

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 'limit' by specifying it as 'Maximum number of rows to retrieve from the query' with a default of 1000, which compensates for the 0% schema description coverage. This clarifies the parameter's purpose beyond the schema's basic type and title, though it could elaborate on implications (e.g., how rows relate to dates or DEXs).

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 weekly trading volume by decentralized exchange'), resource ('on the Solana blockchain'), and scope ('from a Dune Analytics query'). It distinguishes from siblings by specifying 'weekly' frequency and 'Solana' blockchain, unlike other tools with different frequencies (daily, monthly, yearly) or scopes (by chain, frontend, etc.).

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 weekly Solana DEX volume data but does not explicitly state when to use this tool versus alternatives like get_weekly_trading_volume_by_chain or get_daily_trading_volume_by_dex. It mentions the data source (Dune Analytics) and output format, which provides some context, but lacks clear guidance on exclusions or specific scenarios favoring this tool over siblings.

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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