Firefly MCP Server

What does “Firefly” MCP Server do?

The Firefly MCP (Model Context Protocol) Server is a TypeScript-based server designed to integrate with the Firefly platform, enabling seamless connection between AI assistants and your Cloud and SaaS environments. Its key role is to allow AI clients to discover, manage, and codify resources from connected accounts, such as AWS or other cloud providers. By exposing resource discovery and codification capabilities, Firefly MCP empowers AI-driven workflows for tasks like infrastructure management and automation. The server supports secure authentication and is built for easy integration with development tools, including Claude and Cursor, enhancing developer productivity by allowing natural language queries and Infrastructure as Code generation.

List of Prompts

• No explicit prompt templates are documented in the repository.

List of Resources

• Resource Discovery: Exposes all resources across your connected Cloud and SaaS accounts for AI-assisted queries.
• Resource Codification: Allows discovered resources to be represented as Infrastructure as Code (e.g., Terraform templates).
• Secure Authentication: Utilizes access keys to securely interact with Firefly-managed resources.

List of Tools

• No explicit tools are listed in the repository files (such as server.py or an equivalent TypeScript file).

Use Cases of this MCP Server

• Cloud Resource Discovery: Query and list all resources (e.g., EC2 instances) across your AWS and other cloud accounts through natural language.
• Infrastructure as Code Generation: Automatically codify discovered resources into Terraform or other IaC formats, saving engineering time.
• Secure Multi-Cloud Management: Manage resources across multiple cloud and SaaS providers with secure, unified authentication.
• Integration with AI Development Tools: Use Cursor, Claude, or similar tools to leverage Firefly MCP’s capabilities in your development workflows.
• Automation of Infrastructure Tasks: Enable AI agents to automate repetitive infrastructure management tasks, improving efficiency and reducing errors.

How to set it up

Windsurf

1. Ensure Node.js (v14+) and npm/yarn are installed.
2. Generate Firefly access keys from your Firefly account.
3. Install the MCP server using npx:

   npx @fireflyai/firefly-mcp
   

4. Update your mcp.json configuration:

   {
     "mcpServers": {
       "firefly": {
         "command": "npx",
         "args": ["-y", "@fireflyai/firefly-mcp"],
         "env": {
           "FIREFLY_ACCESS_KEY": "your_access_key",
           "FIREFLY_SECRET_KEY": "your_secret_key"
         }
       }
     }
   }
   

5. Save changes and restart Windsurf if necessary.

Claude

1. Prerequisites: Node.js (v14+) and Firefly access keys.
2. Start MCP server:

   npx @fireflyai/firefly-mcp --sse --port 6001
   

3. Add to your Claude configuration:

   {
     "mcpServers": {
       "firefly": {
         "url": "http://localhost:6001/sse"
       }
     }
   }
   

4. Save and restart Claude. Verify integration.

Cursor

1. Install Node.js and obtain Firefly credentials.
2. Run the server as described above.
3. In Cursor, connect to the MCP server as per Cursor’s Model Context Protocol docs.
4. Example configuration:

   {
     "mcpServers": {
       "firefly": {
         "url": "http://localhost:6001/sse"
       }
     }
   }
   

5. Use Cursor’s extension to interact with Firefly MCP.

Cline

1. Set up Node.js and Firefly credentials.
2. Start MCP server:

   npx @fireflyai/firefly-mcp --sse --port 6001
   

3. In the configuration file (mcp.json), add:

   {
     "mcpServers": {
       "firefly": {
         "url": "http://localhost:6001/sse"
       }
     }
   }
   

4. Save and restart Cline to apply changes.

Securing API Keys

Always keep your access keys secret and prefer environment variables for credentials:

{
  "mcpServers": {
    "firefly": {
      "command": "npx",
      "args": ["-y", "@fireflyai/firefly-mcp"],
      "env": {
        "FIREFLY_ACCESS_KEY": "your_access_key",
        "FIREFLY_SECRET_KEY": "your_secret_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:

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

Overview

Based on the available documentation and repository structure, Firefly MCP offers a solid overview, security guidance, and resource integration, but lacks detail on prompt templates, tools, roots, and sampling features. As such, it is functional but not fully documented for the breadth of MCP capabilities.

Our opinion

MCP Score: 5/10
Firefly MCP covers the basics for setup, usage, and resource integration with clear documentation and a permissive license, but lacks advanced MCP features and detailed tool/prompt support in its public repository.

MCP Score