Ergo MCP Server

What does “Ergo” MCP Server do?

The Ergo MCP (Model Context Protocol) Server is designed to connect AI assistants with the Ergo blockchain ecosystem, enabling seamless access to on-chain data and related services. By acting as a bridge between AI clients and blockchain data, the Ergo MCP Server empowers developers and AI systems to perform tasks such as querying blockchain transactions, analyzing token holders, and interacting with various resources exposed by the Ergo ecosystem. This server enhances development workflows by making it possible for AI agents to integrate blockchain data into their reasoning or automation chains, which is particularly valuable for tasks like data analytics, blockchain monitoring, or automated reporting.

List of Prompts

No prompt templates could be identified in the repository files.

List of Resources

No explicit resources were described in the available files.

List of Tools

No tools could be found in a server.py or equivalent tool-exposing file in the listed repository structure.

Use Cases of this MCP Server

• Blockchain Transaction Analytics
  Developers and AI agents can retrieve and analyze transaction data from the Ergo blockchain, enabling use cases like fraud detection, transaction visualization, or user portfolio tracking.

• Token Holder Monitoring
  The server can potentially facilitate monitoring of token holder distributions and changes over time, which is useful for governance, research, or airdrop planning.

• Blockchain Data Exploration
  Enables interactive exploration of the Ergo blockchain, allowing developers to query specific addresses, blocks, or transactions to gain insights or build dashboards.

• Integration with AI Workflows
  By serving as an MCP server, Ergo data can be directly integrated into AI development environments and workflows, making it possible to automate reporting or trigger actions based on blockchain events.

How to set it up

Windsurf

1. Install Node.js and ensure your environment supports MCP servers.
2. Locate your Windsurf configuration file.
3. Add the Ergo MCP server using the following JSON snippet:

   {
     "mcpServers": {
       "ergo-mcp": {
         "command": "npx",
         "args": ["@ergo/mcp-server@latest"]
       }
     }
   }
   

4. Save changes and restart Windsurf.
5. Confirm the server is running by checking logs or server status.

Claude

1. Ensure Claude is installed and configured for MCP integration.
2. Open Claude’s configuration file.
3. Add the Ergo MCP server:

   {
     "mcpServers": {
       "ergo-mcp": {
         "command": "npx",
         "args": ["@ergo/mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Claude.
5. Verify connectivity.

Cursor

1. Install Node.js and set up Cursor.
2. Open the Cursor configuration file.
3. Insert the server configuration:

   {
     "mcpServers": {
       "ergo-mcp": {
         "command": "npx",
         "args": ["@ergo/mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Cursor.
5. Check the MCP integration is active.

Cline

1. Confirm Cline supports MCP servers and is properly installed.
2. Edit the Cline configuration file.
3. Add the Ergo MCP server:

   {
     "mcpServers": {
       "ergo-mcp": {
         "command": "npx",
         "args": ["@ergo/mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Cline.
5. Ensure the server is accessible.

Securing API Keys

To secure API keys, use environment variables as shown below:

{
  "mcpServers": {
    "ergo-mcp": {
      "command": "npx",
      "args": ["@ergo/mcp-server@latest"],
      "env": {
        "ERGO_API_KEY": "${ERGO_API_KEY}"
      },
      "inputs": {
        "api_key": "${ERGO_API_KEY}"
      }
    }
  }
}

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:

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

Overview

Between these two tables, the Ergo MCP server currently offers basic setup and integration documentation, but lacks information about concrete tools, resources, or prompt templates. Its structure suggests potential, but the absence of explicit tool/resource exposure limits current practical value.
MCP Score: 3/10

MCP Score