Dune Analytics MCP Server

What does “Dune Analytics” MCP Server do?

The Dune Analytics MCP Server is a Model Context Protocol (MCP) server that serves as a bridge between Dune Analytics and AI agents. Its primary purpose is to enable seamless integration of blockchain analytics data into AI-powered workflows by allowing assistants to fetch and execute Dune queries programmatically. By exposing Dune Analytics data as tools, developers and AI clients can perform real-time blockchain data retrieval, analysis, and reporting within their automation or agent workflows. The server’s CSV output format ensures compatibility with downstream data processing, making it valuable for tasks like on-chain analytics, dashboard generation, and automated reporting in developer environments.

List of Prompts

No explicit prompt templates are listed in the repository or documentation.

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List of Resources

No explicit resources are described or exposed in the documentation or code.

List of Tools

• get_latest_result
  • Fetches the latest results of a Dune Analytics query by its ID. The output is a CSV-formatted string containing the query results.
• run_query
  • Executes a specified Dune Analytics query by its ID and retrieves the results. Also returns data in a CSV-formatted string.

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Use Cases of this MCP Server

• Blockchain Data Analytics
  • Automate the retrieval and analysis of blockchain data by running Dune Analytics queries directly from AI agents.
• Dashboard Generation
  • Power dynamic dashboards or reporting tools by fetching and updating Dune Analytics data via the MCP interface.
• Automated Crypto Market Reporting
  • Enable bots or assistants to generate up-to-date reports on blockchain activity, DeFi protocols, or token analytics.
• Data-Driven Smart Contract Monitoring
  • Integrate real-time monitoring for smart contracts or on-chain events by querying Dune for specific triggers and surfacing the results.
• Research and Data Science Workflows
  • Facilitate rapid experimentation and data gathering for crypto research through automated Dune queries embedded in data science pipelines.

How to set it up

Windsurf

1. Ensure you have Python 3.10+ and a valid Dune Analytics API key.
2. In your Windsurf configuration, locate the mcpServers section.
3. Add the Dune Analytics MCP Server with the following JSON snippet:

   {
     "mcpServers": {
       "dune-analytics": {
         "command": "python",
         "args": ["main.py"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the setup by issuing a test Dune query through the interface.

Securing API Keys: Set your DUNE_API_KEY in environment variables. Example:

{
  "mcpServers": {
    "dune-analytics": {
      "command": "python",
      "args": ["main.py"],
      "env": {
        "DUNE_API_KEY": "${DUNE_API_KEY}"
      },
      "inputs": {
        "DUNE_API_KEY": "your_api_key_here"
      }
    }
  }
}

Claude

1. Install the server using Smithery CLI:

   npx -y @smithery/cli install @kukapay/dune-analytics-mcp --client claude
   

2. Ensure your DUNE_API_KEY is set as an environment variable.
3. In Claude Desktop, confirm the MCP server is listed and running.
4. Use the command interface to run Dune queries directly.
5. Verify output is received in CSV format.

Cursor

1. Install Python 3.10+ and obtain a Dune API key.
2. In Cursor’s configuration file, add:

   {
     "mcpServers": {
       "dune-analytics": {
         "command": "python",
         "args": ["main.py"]
       }
     }
   }
   

3. Set your API key in the environment or configuration.
4. Restart Cursor and test the connection.

Cline

1. Make sure Python 3.10+ is installed and your API key is ready.
2. Add the MCP server definition to your Cline config:

   {
     "mcpServers": {
       "dune-analytics": {
         "command": "python",
         "args": ["main.py"]
       }
     }
   }
   

3. Export DUNE_API_KEY in your shell or as an environment variable.
4. Restart Cline and run a sample query to validate.

Securing API Keys (applies to all platforms): Always use environment variables to avoid exposing your API keys in plaintext configuration files.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "dune-analytics": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “dune-analytics” to the actual name and replace the URL with your own MCP server URL.

Overview

Our opinion

The Dune Analytics MCP Server is straightforward and focused on providing access to Dune Analytics queries as tools, making it valuable in blockchain analytics workflows. However, it lacks prompt templates and explicit resources, and does not mention advanced MCP features like roots or sampling. It is well-documented for setup and usage. On a scale of 0–10, we would rate this MCP a 6/10, mainly due to its clear utility and good documentation but limited breadth in MCP feature support.

MCP Score