Tools: MCP

Build with LLMs

The GoCardless Model Context Protocol (MCP) tool enables Large Language Models (LLMs) like Claude or Copilot to provide intelligent guidance when building GoCardless integrations. It gives your LLM deep, structured knowledge of the GoCardless API, optimal integration patterns, and code samples across all supported languages.

The MCP also lets you query your live GoCardless account data directly from your LLM — see Query your account data below.

What is MCP?

Model Context Protocol provides a standardised, machine-readable schema that allows LLMs to "understand" our API's capabilities, write code, and make API calls in a context-aware manner. You can connect your LLM to the GoCardless MCP to access up-to-date, comprehensive integration guidance that has been optimised for LLMs.

How it works

The GoCardless MCP provides two capabilities:

• Integration guidance: Your LLM can query comprehensive information about GoCardless endpoints, integration patterns, webhooks, and code samples.

• Account data queries: With authentication, your LLM can perform read-only lookups against your live GoCardless account — checking payment statuses, listing customers, viewing payouts, and more. See Query your account data for details.

Connect

Ensure your LLM is using the MCP

Some LLMs have a tendency to search the web instead of using an MCP. Be sure to give the LLM this prompt to ensure it is making use of the MCP:

For any prompt that mentions GoCardless, use the GoCardless MCP.

Switching environment

If or when you want to switch your connection from Sandbox to Live (or vice versa), the exact steps will vary based on which LLM/IDE you’re using but generally you must:

1. Find the GoCardless MCP in your LLM.

2. Disconnect from it.

3. Connect again, following the steps above.

4. This will trigger the sign-in journey, allowing you to choose (and sign into) the other environment.

If you are not sure which environment you are currently connected to you can ask your LLM.

What you can do with it

Where the MCP helps

• Integration development: Generate integration code, understand API parameters, and get code samples.

• Payment pages implementation: Set up hosted/custom payment pages, configure redirects, and handle different payment types.

• Best practice guidance: On integration approaches, error handling, webhooks, security, and compliance.

Example prompts and workflows:

• Getting started: "How do I collect recurring payments for new users that sign-up on my website using GoCardless?"

• Code generation: "Write code in Python that creates a billing request for a £30 monthly recurring payment and redirects the user to GoCardless hosted payment pages"

• Integration patterns: "I own a gym. How do I collect a £30 joining fee plus a £50 per month membership fee using GoCardless?"

• Specific implementations: "Show me how to handle GoCardless webhooks in Node.js to know when a mandate becomes active"

• Troubleshooting: "My billing request flow is failing - what are the common issues and how do I debug them?"

Query your account data

The MCP also provides read-only access to your GoCardless account. Once signed in, your LLM can look up payments, customers, mandates, subscriptions, payouts, refunds, and events on your behalf. This is useful for quickly checking payment statuses, debugging failed payments, or reviewing recent payouts without leaving your development environment.

All queries are strictly read-only — the MCP cannot create, cancel, or modify any resources. Sensitive customer data (email addresses, phone numbers, bank details) is automatically masked in responses.

Example queries:

• "Why did payment PM01K44FZ685ZVKP4E6J9C3NPHJX fail?"

• "Show me all confirmed payments from this month"

• "How much did I receive in payouts last week?"

• "What active subscriptions does customer CU01KNVS3PYJVAC8ZEHKK8EBZW7K have?"

For a full guide to account queries — including more examples, tips, and troubleshooting — see Ask AI about your account.

Best practices

Getting the most from the MCP

• Tell your LLM to use it: see the Ensure your LLM is using the MCP section above

• Be specific: Include details about your use case, industry, and payment patterns

• Specify language: Mention your preferred programming language upfront

• Share context: Provide relevant code or error messages when troubleshooting. 

• Iterate: Start with basic implementation, then refine based on testing

Using the LLM in your editor with the relevant project open is strongly recommended.

Considerations

• Never share your API keys with your LLM

• Use sandbox credentials during development

• Always review generated code before executing

• Implement proper error handling and validation

Free & Consumer-Paid Tier LLM Privacy Notice

Using free or personal paid tiers (like "Plus" or "Pro" plans) on LLMs often means your conversations may be used for AI training, meaning your information and discussions could become part of their future datasets.

To protect yourself, review the provider's privacy policy and look for opt-out options in your account settings. For stricter data privacy where your data is contractually excluded from training, you typically need an Enterprise or Business subscription.

Questions and feedback

If you have questions, need support or have an idea for improvement please let us know here.