AI coding agents can search and reference the MoonPay Platform documentation directly from your development environment. The docs provide four integration points: an MCP server for real-time search, agent skills for structured capabilities,Documentation Index
Fetch the complete documentation index at: https://dev.moonpay.com/llms.txt
Use this file to discover all available pages before exploring further.
llms.txt files for bulk context, and contextual code actions on
every code block.
MCP server
The documentation includes a Model Context Protocol (MCP) server that gives AI agents direct access to search and retrieve content. Instead of relying on web search, your agent queries the documentation index for accurate, up-to-date results. The server URL is:MCP server URL
Connect your client
- Claude Code
- Cursor
- Claude Desktop
- Codex
- Other clients
Run the following command to add the MCP server to Claude Code:By default this adds the server to your local scope. Use
Add MCP server
--scope project
to share the configuration with your team via .mcp.json, or
--scope user to make it available across all your projects.Skills
The documentation publishes aSKILL.md file that describes MoonPay Platform
capabilities in a structured, machine-readable format. Unlike the MCP server,
which responds to individual queries, SKILL.md gives an agent a complete
picture of available workflows, required inputs, and constraints up front.
View the generated file at
dev.moonpay.com/SKILL.md.
Install skills
Run the following command to add MoonPay Platform skills to your agent’s context:skill.md file automatically. Once
installed, your agent can reference MoonPay Platform capabilities without
additional configuration.
Contextual code actions
Code blocks throughout these docs include contextual actions for sending code directly to an AI tool. Hover over any code block to see options for Cursor, Claude, and ChatGPT. Each option copies the code along with surrounding documentation context so the AI tool understands how to use it.llms.txt
The documentation providesllms.txt files for direct content
ingestion by large language models:
| File | Description |
|---|---|
llms.txt | A concise summary of the documentation structure and key pages |
llms-full.txt | The complete documentation content for full-context indexing |