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get_trending_tokens_by_kol_trading_volume

Identify trending Solana memecoins by analyzing trading activity from key opinion leaders (KOLs). Retrieve a formatted table with token names, mint addresses, KOL buys, total buys, and trading volume for informed decision-making.

Instructions

Retrieve tokens with the highest trading volume by memecoin KOLs.

Args:
    limit (int): Maximum number of tokens to return. Defaults to 100.

Returns:
    str: A formatted table of trending tokens by KOL trading volume including
        token name, mint address, unique KOL buys, total buys, and total volume,
        or an error message if the query fails.

Raises:
    httpx.HTTPStatusError: If the Dune API request fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Implementation Reference

  • main.py:222-252 (handler)
    The handler function for the 'get_trending_tokens_by_kol_trading_volume' tool, decorated with @mcp.tool() for registration. It fetches latest results from Dune query ID 4838351, processes the rows into a formatted table using tabulate, and returns the markdown table or error.
    @mcp.tool()
    def get_trending_tokens_by_kol_trading_volume(limit: int = 100) -> str:
        """Retrieve tokens with the highest trading volume by memecoin KOLs.
    
        Args:
            limit (int): Maximum number of tokens to return. Defaults to 100.
    
        Returns:
            str: A formatted table of trending tokens by KOL trading volume including
                token name, mint address, unique KOL buys, total buys, and total volume,
                or an error message if the query fails.
    
        Raises:
            httpx.HTTPStatusError: If the Dune API request fails.
        """
        try:
            data = get_latest_result(4838351, limit=limit)
            rows = [
                [
                    row["token"],
                    row["contract_address"],
                    row["unique_kols"],
                    row["total_buys"],
                    f'${row["total_volume"]:.2f}'
                ]
                for row in data
            ]
            headers = ["Token", "Mint Address", "Unique KOL Buys", "Total Buys", "Total Volume"]
            return f"# Top {limit} Trending Tokens by KOL Trading Volume\n\n" + tabulate(rows, headers=headers)
        except Exception as e:
            return str(e)
  • main.py:23-46 (helper)
    Helper function used by the tool to fetch the latest execution results from a specified Dune Analytics query using the Dune API.
    def get_latest_result(query_id: int, limit: int = 1000):
        """
        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 does well by disclosing key behaviors: it describes the return format (formatted table with specific columns), mentions error handling (error message if query fails), and reveals external dependencies (Dune API with httpx.HTTPStatusError potential). It doesn't cover rate limits, caching, or authentication needs, but provides substantial operational context.

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 with a clear purpose statement followed by organized sections (Args, Returns, Raises). Every sentence adds value: the first defines the tool's function, and subsequent sections provide essential usage details without redundancy. It's front-loaded with the core functionality.

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, parameter semantics, return format, and error conditions. However, it lacks explicit guidance on when to choose this tool over siblings, and doesn't mention performance characteristics like response time or data freshness, leaving minor gaps.

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 beyond the input schema's 0% coverage. It explains that 'limit' controls the 'maximum number of tokens to return' and provides the default value (100), which clarifies the parameter's purpose and typical usage. Since schema coverage is 0%, this compensation is effective, though it doesn't detail constraints like minimum/maximum values.

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 tokens with the highest trading volume by memecoin KOLs'), identifies the resource (tokens), and distinguishes from siblings by specifying the unique KOL trading volume focus. It's more specific than general trending token tools like get_trending_tokens_by_source or get_trending_tokens_on_pumpswap.

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 'tokens with the highest trading volume by memecoin KOLs,' suggesting this tool is for analyzing influencer-driven trading activity. However, it doesn't explicitly state when to use this versus alternatives like get_recent_kol_buys (which might show individual buys) or other trending token tools that use different metrics (e.g., market cap or general volume).

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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