Model Context Protocol (MCP) server

Empower LLMs to debug issues, automate billing operations, and integrate Paddle faster without writing code or navigating dashboards.

Connect AI assistants like Claude and Cursor directly to your Paddle account to achieve complex flows across Paddle and other platforms in seconds.

Describe what you need in plain language to build complete pricing models, investigate failed payments, integrate Paddle features, or process customer refunds — all while you stay in the flow of your work.

How it works

Managing a billing platform traditionally means switching between dashboards, reading API documentation, and writing code to perform operations. Even simple tasks like creating a new pricing tier or investigating a failed payment require multiple steps across different interfaces.

By connecting the Paddle Model Context Protocol (MCP) server to your AI assistants, you can describe what you need in conversation to take action. The assistant handles any complexity by querying the right data, making the necessary changes, and explaining the results in plain language.

Example use cases

  • Integrate Paddle

    Implement, debug, and test frontend and backend Paddle implementations in a matter of minutes.

  • Build and evolve your pricing

    Create complete pricing models or catalogs with regional variations by describing your structure.

  • Investigate billing issues

    Ask about specific customer problems and get complete transaction and subscription histories with explanations.

  • Onboard enterprise customers

    Migrate and set up enterprise customers with custom pricing and manual invoicing. Use in conjunction with quote management tools and CRMs.

  • Handle subscription changes

    Handle subscription changes and transaction adjustments intelligently with context, recommendation, and action.

  • Analyze company performance

    Generate custom reports and get insights about revenue, churn, and customer behavior.

Your experience

  1. You describe what you need

    Ask your AI assistant about billing operations, customer issues, integration tasks, or anything Paddle-related. Describe what you're trying to achieve in plain language.

  2. Assistant identifies tools to use

    The assistant selects which tools are needed to fulfill your use case. There can be multiple.

     

    It may ask follow-up questions to clarify your request, gain required details, or better understand your use case.

  3. You review before actions execute

    Most assistants verify their actions before accessing data or making changes to your account.

     

    You can approve, request changes, or ask questions. Nothing happens without your confirmation by default.

  4. Assistant executes and explains

    Products get created, issues get resolved, code gets written, or reports get generated. The assistant explains the results and provides insights when relevant.

     

    The full context is maintained to ask follow-up questions, request adjustments, or work through complex scenarios without starting over.

Next steps

AI AssistantBeta

Ask a question to start a conversation!

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