Paddle MCP Server Integration

What does “Paddle” MCP Server do?

The Paddle MCP (Model Context Protocol) Server is a bridge between AI assistants and the Paddle API, enabling streamlined management of product catalogs, billing, subscriptions, and financial reports. By exposing Paddle’s rich set of commerce and billing functionalities through MCP, it allows AI-powered tools like Claude, Cursor, or Windsurf to securely interact with Paddle’s APIs. This integration allows for intelligent automation of developer workflows such as querying products, creating new catalog entries, managing customers, or generating business reports. By offloading these tasks to the Paddle MCP Server, developers and AI agents can quickly access up-to-date billing and product information, manage pricing, and perform complex operations without manual intervention, thus enhancing efficiency and accuracy in SaaS product development and operations.

List of Prompts

No prompt templates are explicitly mentioned in the repository or documentation.

List of Resources

No explicit MCP resources are mentioned in the repository or documentation.

List of Tools

Based on the README and features, the following tools are implied to be provided by the Paddle MCP Server:

• List products: Retrieves the list of products in your Paddle catalog.
• Create product: Adds a new product to your Paddle catalog.
• List prices: Retrieves pricing information for products.
• Create price: Adds a new price for an existing product.
• List customers: Retrieves a list of customers.
• List transactions: Retrieves a list of transactions.
• List subscriptions: Retrieves all subscriptions managed in Paddle.
• Create custom report: Generates custom financial analysis reports.

Use Cases of this MCP Server

• Product Catalog Management: Easily list, create, and update products in your Paddle catalog using AI assistants or IDE plugins, streamlining catalog operations and reducing manual errors.
• Subscription & Billing Operations: Quickly access, list, and manage subscriptions and customer billing details, making it easier to handle SaaS billing logic within development environments.
• Financial Reporting: Create and retrieve financial and business reports for analytics or audits directly via AI tools, automating complex reporting tasks.
• Customer Support Automation: Enable AI-driven support workflows to fetch customer data, resolve billing queries, or update customer records, improving support efficiency.
• API-driven Commerce Integrations: Use the MCP server as an automation layer for integrating Paddle’s billing and commerce capabilities into custom apps, bots, or internal tools.

How to set it up

Windsurf

1. Prerequisites: Ensure Node.js is installed.
2. Obtain Paddle API Key: Create/manage your API key in Paddle’s Developer tools.
3. Edit Configuration: Add the Paddle MCP server entry to your Windsurf MCP config.
4. Insert JSON Snippet:

   {
     "mcpServers": {
       "paddle": {
         "command": "npx",
         "args": ["-y", "@paddle/paddle-mcp", "--api-key=PADDLE_API_KEY", "--environment=sandbox"]
       }
     }
   }
   

5. Save & Restart: Save the configuration and restart Windsurf.
6. Verify: Check for Paddle MCP server availability in Windsurf.

Securing API Keys

Example using environment variables:

{
  "mcpServers": {
    "paddle": {
      "command": "npx",
      "args": ["-y", "@paddle/paddle-mcp"],
      "env": {
        "PADDLE_API_KEY": "your_api_key",
        "PADDLE_ENVIRONMENT": "sandbox"
      }
    }
  }
}

Claude

1. Prerequisites: Ensure Node.js is installed.
2. Get API Key: Obtain from Paddle Developer tools.
3. Edit MCP Config: Add the Paddle MCP JSON to your config.
4. Insert JSON Snippet:

   {
     "mcpServers": {
       "paddle": {
         "command": "npx",
         "args": ["-y", "@paddle/paddle-mcp", "--api-key=PADDLE_API_KEY", "--environment=production"]
       }
     }
   }
   

5. Save & Restart Claude.

Securing API Keys

Use environment variables as shown above.

Cursor

1. Prerequisites: Node.js installed.
2. Get API Key: From Paddle Developer tools.
3. Edit Config: Add the MCP server to Cursor’s config.
4. Insert JSON Snippet:

   {
     "mcpServers": {
       "paddle": {
         "command": "npx",
         "args": ["-y", "@paddle/paddle-mcp", "--api-key=PADDLE_API_KEY", "--environment=sandbox"]
       }
     }
   }
   

5. Restart Cursor and verify Paddle MCP integration.

Securing API Keys

Use the environment-based approach as above.

Cline

1. Prerequisites: Ensure Node.js is installed.
2. API Key: Obtain from Paddle’s Developer tools.
3. Config File: Add Paddle MCP entry to the config.
4. Example JSON:

   {
     "mcpServers": {
       "paddle": {
         "command": "npx",
         "args": ["-y", "@paddle/paddle-mcp", "--api-key=PADDLE_API_KEY", "--environment=sandbox"]
       }
     }
   }
   

5. Save & Restart Cline.

Securing API Keys

Use environment variables as described above.

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:

{
  "paddle": {
    "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 “paddle” 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 information, the Paddle MCP server provides a solid set of tools and setup instructions, but lacks explicit prompt templates and resource definitions in its documentation. Its security guidance is clear, and the feature set is well matched to Paddle’s API. The absence of roots and sampling support documentation is a minor gap.

MCP Score

Overall, I would rate this MCP server a 6/10. It covers the essentials for Paddle API automation, provides clear setup and security guidance, and exposes key tooling, but lacks advanced MCP features like resources, prompt templates, roots, and sampling support in its documentation.