Algorand MCP Server

What does “Algorand” MCP Server do?

The Algorand MCP (Model Context Protocol) Server is a tool designed to connect AI assistants with external data sources, APIs, and services, specifically focusing on the Algorand blockchain ecosystem. By leveraging the MCP specification, it enables developers and AI agents to interact with Algorand resources such as blockchain data, smart contracts, or account information. This server streamlines complex workflows, allowing tasks like querying blockchain states, managing transactions, or integrating Algorand data into broader AI-driven solutions. Its primary role is to enhance development workflows by making the Algorand blockchain more accessible and actionable through standardized LLM interactions.

List of Prompts

No prompt templates are mentioned in the repository.

List of Resources

No specific MCP “resources” are listed in the available documentation or files.

List of Tools

No explicit tools are listed in the repository files or documentation.

Use Cases of this MCP Server

• Blockchain Data Retrieval: Enables developers and AI agents to access real-time or historical data from the Algorand blockchain, supporting analytics, monitoring, or DApp development.
• Smart Contract Interaction: Facilitates communication with Algorand smart contracts, making it easier to automate contract calls or manage permissions within AI-driven workflows.
• Transaction Management: Assists in creating, signing, and broadcasting transactions on the Algorand network, streamlining wallet and payment solutions for developers.
• Account Information Access: Retrieves account balances, asset holdings, and transaction histories for user accounts, supporting wallet integrations and reporting tools.

How to set it up

Windsurf

1. Ensure Node.js is installed on your system.
2. Open the Windsurf configuration file.
3. Add the Algorand MCP Server to the mcpServers section using the following JSON snippet:

   {
     "algorand-mcp": {
       "command": "npx",
       "args": ["@GoPlausible/algorand-mcp@latest"],
       "env": {
         "ALGOD_API_KEY": "${ALGOD_API_KEY}"
       }
     }
   }
   

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

Claude

1. Install Node.js if required.
2. Edit the Claude configuration file.
3. Insert the Algorand MCP Server configuration:

   {
     "mcpServers": {
       "algorand-mcp": {
         "command": "npx",
         "args": ["@GoPlausible/algorand-mcp@latest"],
         "env": {
           "ALGOD_API_KEY": "${ALGOD_API_KEY}"
         }
       }
     }
   }
   

4. Save changes and restart Claude.
5. Confirm server integration.

Cursor

1. Set up Node.js in your environment.
2. Navigate to the Cursor configuration file.
3. Add the following to your mcpServers section:

   {
     "algorand-mcp": {
       "command": "npx",
       "args": ["@GoPlausible/algorand-mcp@latest"],
       "env": {
         "ALGOD_API_KEY": "${ALGOD_API_KEY}"
       }
     }
   }
   

4. Restart Cursor after saving.
5. Check for connectivity.

Cline

1. Make sure Node.js is present.
2. Edit the Cline configuration as follows:

   {
     "mcpServers": {
       "algorand-mcp": {
         "command": "npx",
         "args": ["@GoPlausible/algorand-mcp@latest"],
         "env": {
           "ALGOD_API_KEY": "${ALGOD_API_KEY}"
         }
       }
     }
   }
   

3. Save and restart Cline.
4. Ensure the MCP server is operational.

Securing API Keys

Store sensitive API keys (like ALGOD_API_KEY) in environment variables instead of hardcoding. Example:

{
  "algorand-mcp": {
    "command": "npx",
    "args": ["@GoPlausible/algorand-mcp@latest"],
    "env": {
      "ALGOD_API_KEY": "${ALGOD_API_KEY}"
    },
    "inputs": {}
  }
}

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:

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

Overview

Based on the above tables, the Algorand MCP implementation provides clear setup instructions and an overview, but is missing documentation for prompts, resources, tools, or advanced features like sampling support. Its open licensing and moderate popularity are positives for adoption, but the lack of feature documentation lowers its score.

MCP Score