Make MCP Server Integration

What does “Make” MCP Server do?

The Make MCP (Model Context Protocol) Server bridges AI assistants with the Make automation platform, allowing AI systems to trigger and interact with Make scenarios as callable tools. It connects to your Make account, identifies all scenarios set up for “On-Demand” scheduling, and exposes them as functions that AI can use. The server parses scenario input parameters, returns structured JSON outputs, and enables AI assistants to invoke complex automation workflows seamlessly. By integrating Make with AI assistants, developers can maintain sophisticated automation logic within Make while granting AI systems the ability to execute and manage these workflows, thus enhancing development productivity and enabling powerful bidirectional communication between AI and automation platforms.

List of Prompts

No information on prompt templates is provided in the repository.

List of Resources

No explicit resources are described in the repository.

List of Tools

• Make Scenario Invocation: Allows AI assistants to invoke any “On-Demand” Make scenario, passing in parameters and receiving structured output. This effectively turns each scenario into an AI-callable tool, enabling a wide range of automations to be triggered programmatically.

Use Cases of this MCP Server

• Automated Workflow Execution: Developers can use AI to trigger Make scenarios that automate tasks such as data processing, notifications, or multi-step integrations.
• AI-Driven Data Pipelines: AI assistants can fetch and process data across multiple apps by invoking Make scenarios, streamlining ETL and integration workflows.
• Task Orchestration: Use AI to coordinate complex, multi-system tasks by chaining Make scenarios together, leveraging the server as a universal automation interface.
• Dynamic Parameter Resolution: AI can dynamically compute and supply parameters to Make scenarios, making automations more adaptive and context-aware.
• Business Process Automation: Enable AI to initiate, monitor, or adjust business workflows (like lead management or ticketing) managed in Make, directly from conversational interfaces.

How to set it up

Windsurf

No setup instructions for Windsurf are provided.

Claude

1. Prerequisites: Install Node.js and obtain your Make API Key with scenarios:read and scenarios:run scopes.
2. Locate Configuration: Open your claude_desktop_config.json file.
3. Add MCP Server: Insert the following to the mcpServers section:

   {
     "mcpServers": {
       "make": {
         "command": "npx",
         "args": ["-y", "@makehq/mcp-server"],
         "env": {
           "MAKE_API_KEY": "<your-api-key>",
           "MAKE_ZONE": "<your-zone>",
           "MAKE_TEAM": "<your-team-id>"
         }
       }
     }
   }
   

4. Save and Restart: Save the config and restart Claude Desktop.
5. Verify: Ensure the MCP server appears in your list of available tools.

Securing API Keys:
Store sensitive values in the env object as shown above.
Example:

"env": {
  "MAKE_API_KEY": "<your-api-key>",
  "MAKE_ZONE": "<your-zone>",
  "MAKE_TEAM": "<your-team-id>"
}

Cursor

No setup instructions for Cursor are provided.

Cline

No setup instructions for Cline are provided.

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:

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

Overview

Our opinion

The Make MCP Server provides a simple, focused bridge between AI assistants and the Make automation platform. While its documentation is minimal and lacks details on prompt workflows and explicit resources, its tool integration is robust for scenario invocation. The inclusion of security/environment variable practices is a plus. The project would score higher with more detailed docs and broader MCP feature support.

MCP Score