From prompt to prototype: AI tools for developers

Going from ideation to a working prototype shouldn’t be the hard part. If you’re building with Plaid, chances are you want to build quickly, test an idea, and validate it immediately. That’s why we’re rolling out our AI Development Toolkit, a set of new AI tools to help you get from 0 to 1 faster. These tools are built to work seamlessly with the AI-powered developer environments you already use like Cursor, Claude Code, and Windsurf. 

Here’s what’s in the toolkit:

• AI Integration Guides to help you kick-start a working Plaid integration

• A sandbox MCP server to help you validate your integration locally

Whether you’re just getting started with Plaid or pushing to ship, these tools are built to help you move quickly and iterate with confidence.

What’s New

AI Integration Guides

The first step to building an integration with Plaid is learning our documentation and piecing together code snippets - which can sometimes be time consuming. So we built AI Integration Guides to make the first step faster and way easier. We’ve restructured our documentation to be more LLM-friendly and built a library of prompts to give your agent the context it needs to generate working Plaid integrations - from setting up Link to token exchange. 

The first set of guides we’re publishing cover common use cases and integrations:

• Plaid Transactions - Fetch categorized transaction data

• Plaid Transfer - Build and test account-to-account money movement

• Plaid Signal - Assess risk before initiating ACH transfers

How it works: you start with one of our pre-built prompts, choose your tech stack, and let your AI assistant help generate the integration code. 

Sandbox MCP Server

To support local development and testing, we’re also introducing the Plaid sandbox MCP server. It runs in your local development environment and allows your AI tools to interact with Plaid’s sandbox APIs in context-aware ways.

You can use it to:

• Generate custom test users using our custom user feature

• Trigger Plaid webhook events to test how your app handles updates

• Generate sandbox tokens without having to write your own code

• Improve search results returned by your AI assistant on Plaid docs

How to Use the AI Toolkit

Imagine you’re building a feature that helps users track their spending, such as a budget dashboard in a personal finance app.

You open your AI-powered IDE and prompt your assistant to generate a Plaid integration using our transactions guide. Our guide gives the model the context it needs to set up each key part of the integration, including setting up Plaid Link, exchanging the public token for an access token, and writing the logic to fetch transaction data. One of the benefits of this workflow is that you’re not limited to a specific stack. You can get a quickstart up and running with the frontend and backend frameworks you already use. 

With your integration live in the sandbox environment, you may want to test a specific scenario that isn’t represented in Plaid’s standard sandbox data. This is where the sandbox MCP server comes in. You can ask your AI assistant to create a custom user fitting specific criteria in natural language rather than in json format. For instance, you can specify that you want to create a custom user with transactions that reflect someone who receives biweekly paychecks, pays rent once a month, and regularly shops on weekends. The AI then chains together the get_mock_data_prompt and get_sandbox_access_token tools from the sandbox MCP server to create an access token for a custom user fitting your specifications that you can reuse across multiple requests.

With that data in place, you can begin testing how your integration responds to realistic patterns. You can preview how your dashboard handles transaction categories, check that charts update based on different spending behaviors, and verify your UI logic behaves as expected. If you want to go deeper, you can also use other Plaid sandbox tools, like the simulate_webhook tool to test trigger transactions update scenarios.

Our toolkit helps you accelerate building, iterating, and testing. The AI handles the setup, while the sandbox environment provides a safe space to experiment and refine your integration.

Get Started

This adds to a suite of new tools we’ve introduced over the past month to help developers get more out of their existing Plaid integration. This includes our Dashboard MCP server tools which enable you to access Plaid analytics and diagnostics data through both a conversational interface in Claude.ai and through custom agents built with large language model (Anthropic and OpenAI) APIs. 

You can try everything we’ve launched today using the links below:

• Explore the integration guides on GitHub

• Set up the sandbox MCP server

• Read the docs

We’re continuing to build new guides, support more use cases, and expand access to additional tools. If you have feedback or ideas, we’d love to hear them. We’re excited to see what you build.

We’ll be hosting a deep dive session on our MCP Servers at Plaid Effects—register to learn more.