wxflows MCP Server Integration

What does “wxflows” MCP Server do?

The wxflows MCP (Model Context Protocol) Server is designed to serve as a bridge between AI assistants and a variety of external data sources, APIs, or services. By leveraging the MCP standard, wxflows enables the secure and modular integration of AI-driven workflows with real-world systems, enhancing the development experience for AI-powered applications. Its key role is to facilitate tasks such as database queries, file management, or making API calls, all through a unified interface. This empowers developers to create, manage, and automate workflows that can access up-to-date information or perform operations on external systems, with AI agents seamlessly orchestrating these actions within their development environment.

Use Cases of this MCP Server

How to set it up

Windsurf

1. Ensure Node.js is installed and your development environment is ready.
2. Open your Windsurf configuration file (typically windsurf.json or similar).
3. Add the wxflows MCP Server with the following JSON snippet:

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

4. Save the configuration file and restart Windsurf.
5. Verify the server is running by checking the Windsurf logs or interface.

Claude

1. Confirm you have Claude installed and configured.
2. Locate the Claude configuration file (claude.config.json or similar).
3. Add the wxflows MCP Server entry:

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

4. Save changes and restart Claude.
5. Confirm server availability in the Claude dashboard.

Cursor

1. Install Node.js and ensure Cursor is set up.
2. Edit Cursor’s configuration file.
3. Insert the MCP server configuration:

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

4. Restart Cursor for changes to take effect.
5. Validate in the Cursor UI.

Cline

1. Set up Node.js and the Cline environment.
2. Access your Cline configuration.
3. Add the MCP server block:

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

4. Save and restart Cline.
5. Check connectivity through Cline’s interface.

Securing API Keys
To secure API keys or credentials, use environment variables in the configuration:

{
  "mcpServers": {
    "wxflows": {
      "command": "npx",
      "args": ["@wxflows/mcp-server@latest"],
      "env": {
        "API_KEY": "${WXFLOWS_API_KEY}"
      },
      "inputs": {}
    }
  }
}

Replace "API_KEY": "${WXFLOWS_API_KEY}" with your specific secret names.

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:

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

Overview

Between the two tables, my overall rating for this MCP repository’s documentation and discoverability based on available information is 2/10. Most key details about prompts, tools, and resources are missing, though setup instructions are clear.

MCP Score