Dune Query MCP

DuneLink

Version Python

A modern bridge connecting Dune Analytics data to intelligent agents through Model Control Protocol (MCP).

Overview

DuneLink enables seamless integration of blockchain data analytics into your AI applications. By leveraging Dune Analytics' powerful query capabilities with the Model Control Protocol, this service allows LLMs and other AI systems to access on-chain data through simple, natural language interactions.

Core Capabilities

Data Retrieval Tools

Tool	Description	Use Case
`get_latest_result`	Retrieves pre-computed query results	Quick access to existing data
`run_query`	Executes a query on-demand	Real-time data analysis

Data Format

All data is returned in CSV format, providing:

Universal compatibility
Easy parsing by most data analysis tools
Human-readable output

Getting Started

System Requirements

Python 3.10 or higher
Valid Dune Analytics API key (Get yours here)

Quick Setup

Clone & Navigate
git clone https://github.com/olaxbt/dune-query-mcp.git cd dunelink
Environment Setup
# Create virtual environment python -m venv .venv # Activate it source .venv/bin/activate # Linux/Mac # or .venv\Scripts\activate # Windows # Install dependencies pip install -r requirements.txt
Configure API Access
# Copy example config cp .env.example .env # Edit with your API key echo "DUNE_API_KEY=your_key_here" > .env

Usage

Running the Service

DuneLink offers two ways to run:

As MCP Service

python run.py

This starts the MCP service on default port 8000.

As Web Server

python flask_app.py

This provides access to the web interface and REST API endpoints.

Integrating with Applications

MCP Client Integration

from mcp.client import Client

# Connect to DuneLink
client = Client("http://localhost:8000")

# Get latest results for a query
csv_data = client.call("get_latest_result", query_id=1234567)

# Execute a query
query_results = client.call("run_query", query_id=1234567)

REST API

Endpoint	Method	Description
`/dune/health`	GET	Service health check
`/dune/query/{query_id}/latest`	GET	Retrieve latest results
`/dune/query/{query_id}/execute`	POST	Run a query

Example REST Call:

curl -X GET http://localhost:5000/dune/query/1234567/latest

Architecture

dunelink/
├── app/                       # Application core
│   ├── __init__.py            # Flask & MCP setup
│   ├── routes/                # API endpoint definitions
│   │   └── dune_routes/       # Dune Analytics routes
│   │   └── templates/             # Web interface
│   └── templates/             # Web interface
├── config/                    # Configuration files
├── logs/                      # Runtime logs
├── flask_app.py               # Web server entry point
├── run.py                     # MCP server entry point
└── requirements.txt           # Dependencies

Advanced Configuration

Environment Variables

Variable	Purpose	Default
DUNE_API_KEY	Authentication for Dune API	None (Required)
PORT	Server port	8000

Performance Tuning

For high-volume query execution:

# Set a higher timeout for long-running queries
export DUNE_QUERY_TIMEOUT=600  # 10 minutes in seconds

Troubleshooting

Common issues and solutions:

Problem	Solution
API Key errors	Ensure `.env` file exists with valid key
Timeout errors	Increase timeout for complex queries
CSV parsing issues	Check query returns proper tabular data

Contributing

Contributions are welcome! Please follow these steps:

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Commit your changes: git commit -am 'Add some amazing feature'
Push to the branch: git push origin feature/amazing-feature
Open a Pull Request

License

This project is released under the MIT License. See LICENSE file for details.

Acknowledgments

Built with FastMCP
Query functionality powered by Dune Analytics

This server cannot be installed

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remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

A bridge that connects Dune Analytics blockchain data to AI applications through Model Control Protocol, allowing LLMs to access on-chain data via natural language interactions.

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