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kukapay

dex-metrics-mcp

get_daily_trading_volume_by_telegram_bot

Fetch daily trading volume data for Telegram bots from Dune Analytics queries and present it in markdown-formatted pivot tables with dates and bot names.

Instructions

Retrieve daily trading volume by Telegram bot.

This tool fetches daily trading volume data for Telegram bots from a Dune Analytics query and returns it in a markdown-formatted pivot table, with dates as the index and bot names 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:241-263 (handler)
    The handler function for the tool 'get_daily_trading_volume_by_telegram_bot'. Decorated with @mcp.tool() for registration. Fetches data from Dune query 2687239, processes with pandas into a pivot table by date and Telegram bot, returns markdown table or error.
    @mcp.tool()
    def get_daily_trading_volume_by_telegram_bot(limit: int = 1000) -> str:
        """
        Retrieve daily trading volume by Telegram bot.
    
        This tool fetches daily trading volume data for Telegram bots from a Dune Analytics query and returns it in a markdown-formatted pivot table, with dates as the index and bot names 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(2687239, limit=limit)
            df = pd.DataFrame(data)
            df["date"] = pd.to_datetime(df["block_date"]).dt.date
            pivot_df = df.pivot(index="date", columns="bot", values="volumeUSD")
            pivot_df = pivot_df.sort_index(ascending=False)
            return pivot_df.to_markdown()
        except Exception as e:
            return str(e)
  • main.py:21-44 (helper)
    Shared 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) -> 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: the tool fetches data from an external source (Dune Analytics query), returns markdown-formatted output (a pivot table with dates as index and bot names as columns), and handles errors (returns an error message if the query fails). However, it doesn't mention potential rate limits, authentication requirements, or data freshness details.

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 zero wasted sentences: a clear purpose statement, elaboration on data source and output format, and separate sections for Args and Returns. Each sentence adds essential information, and the formatting with bullet points enhances readability without unnecessary verbosity.

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?

For a tool with no annotations, no output schema, and minimal parameters, the description provides good completeness: it covers purpose, data source, output format, parameter semantics, and error handling. However, it lacks details on data range (e.g., historical limits), pivot table specifics (e.g., sorting, missing data handling), or performance characteristics that could enhance context for an AI 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 meaningful context for the single parameter 'limit' by explaining it controls 'Maximum number of rows to retrieve from the query' and provides the default value (1000). With 0% schema description coverage and only one parameter, this adequately compensates, though it doesn't elaborate on row semantics or query implications beyond the basic definition.

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 daily trading volume by Telegram bot'), identifies the resource ('daily trading volume data for Telegram bots'), and distinguishes from siblings by specifying the data source ('from a Dune Analytics query') and output format ('markdown-formatted pivot table'). It explicitly mentions bot names as columns, differentiating it from sibling tools that focus on DEX, aggregator, chain, or frontend metrics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: when needing daily trading volume data specifically for Telegram bots from Dune Analytics in a pivot table format. It implicitly distinguishes from siblings by not mentioning alternatives like 'get_daily_trading_volume_by_dex', but lacks explicit guidance on when not to use it or direct comparisons to other tools in the list.

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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