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Building with AI hero Grid’s documentation, OpenAPI spec, and CLI are designed to work with AI coding assistants like Claude Code, Cursor, and Codex. Whether you’re exploring the API for the first time or building a production integration, AI tools can help you move faster.

What AI assistants can do with Grid

AI-accessible documentation

These Grid docs are automatically available to LLMs and AI tools in machine-readable formats — no configuration needed.

llms.txt

Grid docs generate llms.txt files that give AI tools a structured index of all documentation:
  • /llms.txt — Concise index of all pages with titles and descriptions
  • /llms-full.txt — Complete documentation content in a single file
These are generated automatically and always up to date. Use llms-full.txt when you want to give an AI assistant full context about the Grid API in one shot.

Markdown export

Each page in the Grid docs is automatically available as a Markdown file simply by adding .md to the end of the URL. For example, the Building with AI page is available as /platform-overview/building-with-ai.md.
You can also copy any page’s content as Markdown with the keyboard shortcut Command + C (Ctrl + C on Windows) and paste it directly into ChatGPT, Claude, or any AI assistant for context-aware help with your specific question.

Install the Grid API agent skill

The Grid API skill gives Claude Code or another Skill-compatible agent full access to the Grid API via a built-in CLI. Install it with:
Make sure to install it for whichever agent you’re using. For example, if you’re using Claude Code, you’ll need to explicitly select Claude Code in the agent installation selection screen. Once installed, you can start asking questions immediately. To execute API operations, you’ll need to configure your credentials.

Configure your credentials

To set your grid credentials, simply ask the agent to help you configure them:
It’ll prompt you for your API Token ID and Client Secret, validate them, and save to ~/.grid-credentials for future use.
Start in the sandbox environment to experiment safely. The Skill is great at generating fake account data to help you test different flows.

MCP server

Grid provides a Model Context Protocol (MCP) server that lets AI agents search Grid’s SDK documentation and execute Grid API operations directly. Your agentic workflows get full context of the Grid API during implementation and can run quotes, transfers, customer onboarding, and other operations end-to-end.
The MCP server exposes two tools:
  • search_docs — Search Grid SDK documentation for methods, parameters, and usage examples across HTTP, TypeScript, JavaScript, Python, Go, Java, Kotlin, Ruby, and Terraform
  • execute — Interact with the Grid API to create customers, manage accounts, generate and execute quotes, send transfers, look up receivers, and call any other Grid API operation
Because execute calls the live Grid API with your credentials, scope your API token carefully. Use a read-only token in sandbox while experimenting; only issue a write-capable or production-scoped token when you understand the operations the agent will run.

Create an API token

The MCP server requires Grid API credentials. In the Grid dashboard, create a scoped API token — a read-only sandbox token is recommended for first-time setup — and copy the client ID and client secret. You’ll plug these into the x-grid-client-id and x-grid-client-secret headers in the configurations below.

Configure your client

In Claude Code, ask Claude to update your .claude.json config with:

Example prompts

Try these prompts in Claude Code or paste them into your AI assistant of choice:

Getting started

Payouts

On/off-ramps

Account management

Debugging

Tips for best results

  1. Be specific about your use case — “Send a payout to Brazil via PIX” gets better results than “help me send money”
  2. Start with the sandbox — Ask the AI to use sandbox mode so you can experiment without real funds
  3. Give context — Paste the relevant docs page or point the AI to /llms-full.txt for full API context
  4. Iterate on errors — If an API call fails, paste the error and ask the AI to diagnose it