Whale Tracker MCP Server

What does “Whale Tracker” MCP Server do?

The whale-tracker-mcp server is a Python-based implementation of the Model Context Protocol (MCP) that connects AI assistants with the Whale Alert API to enable real-time tracking and analysis of large cryptocurrency transactions, commonly referred to as “whale” movements. By exposing tools, resources, and prompt templates to MCP-compatible clients, this server allows developers, analysts, and cryptocurrency enthusiasts to monitor significant blockchain transfers directly within their LLM-powered workflows. With features like asynchronous API calls, resource exposure, and secure API key management, whale-tracker-mcp enhances AI-driven development by making up-to-date whale activity accessible for contextual awareness, automated analysis, and workflow automation.

List of Prompts

• query_whale_activity: A reusable prompt template for analyzing whale transaction patterns, optionally filtered by blockchain.

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List of Resources

• whale://transactions/{blockchain}: Exposes recent whale transactions for a specified blockchain as contextual data for LLMs and clients.

List of Tools

• get_recent_transactions: Fetches recent whale transactions with optional filters for blockchain, minimum transaction value, and result limit.
• get_transaction_details: Retrieves detailed information about a specific whale transaction by its unique ID.

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Use Cases of this MCP Server

• Real-time Whale Monitoring: Enables developers and analysts to track large cryptocurrency transactions across blockchains in real time, aiding in market sentiment analysis and trading strategies.
• Whale Pattern Analysis: Provides prompt templates and resources for AI assistants to analyze historical and live transaction data, identifying trends and anomalies in whale behavior.
• Automated Alerts & Reporting: Integrate with workflow tools to notify users or trigger actions when significant whale movements are detected.
• Blockchain-Specific Tracking: Filter and expose whale activity on particular blockchains (e.g., Bitcoin, Ethereum), supporting focused research or risk assessment.
• Enrichment of LLM-powered Apps: Supplies contextual whale transaction data to enhance the output and reasoning of AI applications in trading, compliance, or analytics.

How to set it up

Windsurf

1. Ensure you have Node.js and Windsurf installed.
2. Locate your Windsurf configuration file (e.g., windsurf.config.json).
3. Add the Whale Tracker MCP server to the mcpServers section:

   {
     "mcpServers": {
       "whale-tracker": {
         "command": "python",
         "args": ["-m", "whale-tracker-mcp"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the server is running and accessible within Windsurf.

Securing API Keys

Set your Whale Alert API key using environment variables:

{
  "env": {
    "WHALE_ALERT_API_KEY": "<your_api_key_here>"
  }
}

Claude

1. Ensure you have Node.js and Claude Desktop installed.
2. Open your Claude Desktop configuration (usually claude.config.json).
3. Add the Whale Tracker MCP server as follows:

   {
     "mcpServers": {
       "whale-tracker": {
         "command": "python",
         "args": ["-m", "whale-tracker-mcp"]
       }
     }
   }
   

4. Save your configuration and restart Claude Desktop.
5. Confirm integration by checking for whale-tracker in the tool list.

Securing API Keys

Use an environment variable entry:

{
  "env": {
    "WHALE_ALERT_API_KEY": "<your_api_key_here>"
  }
}

Cursor

1. Install Node.js and Cursor.
2. Open your Cursor configuration file (e.g., cursor.config.json).
3. Register the Whale Tracker MCP server:

   {
     "mcpServers": {
       "whale-tracker": {
         "command": "python",
         "args": ["-m", "whale-tracker-mcp"]
       }
     }
   }
   

4. Save the configuration and restart Cursor.
5. Check that the server is available in the Cursor interface.

Securing API Keys

Add your API key securely:

{
  "env": {
    "WHALE_ALERT_API_KEY": "<your_api_key_here>"
  }
}

Cline

1. Ensure Node.js and Cline are installed.
2. Open the Cline configuration (e.g., cline.config.json).
3. Add the Whale Tracker MCP server to the mcpServers block:

   {
     "mcpServers": {
       "whale-tracker": {
         "command": "python",
         "args": ["-m", "whale-tracker-mcp"]
       }
     }
   }
   

4. Save and restart Cline.
5. Verify the server is registered.

Securing API Keys

Set your API key as an environment variable:

{
  "env": {
    "WHALE_ALERT_API_KEY": "<your_api_key_here>"
  }
}

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:

{
  "whale-tracker": {
    "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 “whale-tracker” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

My opinion: Whale Tracker MCP offers a focused, well-documented integration for crypto whale tracking, with clear tooling, prompt, and resource exposure. Its setup and security instructions are thorough for MCP environments. Roots and sampling are not documented. The project is maintained, has stars/forks, and is MIT licensed. I would rate it a 7/10: strong for its purpose and documentation, but missing advanced MCP features.

MCP Score