> ## Documentation Index
> Fetch the complete documentation index at: https://docs.argyle.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Tools

> Use Argyle's machine-readable docs and API resources with AI coding tools.

## What to provide an AI coding tool

AI coding tools do not need to be trained on Argyle. Give them current Argyle context instead: start with `llms.txt`, use the OpenAPI specification for endpoint and schema details, and connect the MCP server when your tool supports MCP.

## Resources

* [llms.txt](https://docs.argyle.com/llms.txt) — Use Argyle's LLM-friendly docs index to find relevant guide, API reference, and OpenAPI pages.
* [OpenAPI specification](https://docs.argyle.com/openAPI/argyle-all.yaml) — Use the consolidated Argyle API spec for generated clients, schema-aware API calls, and endpoint references.
* [Argyle Docs MCP server](https://docs.argyle.com/mcp) — Connect this in MCP-capable tools so the assistant can search and read Argyle docs directly.

## Common ways to use these resources

* In AI coding tools such as Claude Code, Codex, Cursor, and VS Code, add the MCP server if the tool supports remote MCP servers.
* If the tool cannot connect to MCP or does not automatically discover Argyle docs, give it the `llms.txt` link first and ask it to follow the linked Markdown docs for the specific feature.
* For API implementation work, include the OpenAPI specification so the tool can use current endpoint and schema definitions.

## Safety

Keep API keys, secrets, customer data, and borrower data out of AI prompts unless your organization has approved that workflow.