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

# Agent-Readable Surfaces

> Plain-text endpoints an AI agent can fetch to learn what fal offers and how to call it

Every part of fal's catalog and documentation is available as plain text, so an AI agent can read it directly instead of scraping HTML. Each surface is a different granularity or format — pick the smallest one that answers the question. All of them are public: no API key, no authentication header.

<Note>
  If your assistant supports the Model Context Protocol, [Run MCP](/docs/documentation/setting-up/mcp) is usually a better fit than fetching these files: it can search models and check schemas on demand rather than loading a whole document.
</Note>

## The Surfaces

| Surface                                                        | What it contains                                                                           |
| -------------------------------------------------------------- | ------------------------------------------------------------------------------------------ |
| [fal.ai/llms.txt](https://fal.ai/llms.txt)                     | Platform overview: entry points, key concepts, and representative endpoint IDs by category |
| [fal.ai/docs/llms.txt](https://fal.ai/docs/llms.txt)           | An index of every documentation page, each linking to a Markdown rendering                 |
| [fal.ai/docs/llms-full.txt](https://fal.ai/docs/llms-full.txt) | The entire documentation inlined in a single file                                          |
| [fal.ai/docs/sitemap.xml](https://fal.ai/docs/sitemap.xml)     | Machine-readable list of every documentation URL                                           |
| `fal.ai/models/<endpoint-id>/llms.txt`                         | Live schema for one model — see below                                                      |

## Per-Model Schemas

The highest-value surface for an agent mid-task is the per-model file. Append `/llms.txt` to the model page URL, `fal.ai/models/<endpoint-id>` — not to a `/playground` or `/api` variant of it:

```
https://fal.ai/models/fal-ai/flux/dev/llms.txt
```

It returns that endpoint's current input and output schema with types, required and optional fields, defaults, value constraints, pricing, and generated snippets for cURL, Python, and JavaScript. Because it is generated from the same metadata the platform serves, it cannot drift from the real endpoint.

This is one small fetch rather than a whole-catalog download, and it is the reliable way to get an exact schema. Endpoint IDs change as models are versioned, so prefer fetching this file over relying on an ID remembered from training data.

## Reading a Documentation Page as Markdown

Any documentation page can be fetched as Markdown by appending `.md` to its URL:

```
https://fal.ai/docs/documentation/model-apis/inference/queue.md
```

## Suggested Order

1. Start at [fal.ai/llms.txt](https://fal.ai/llms.txt) to learn what the platform does and which models fit the task.
2. Find a specific model in the [model gallery](https://fal.ai/explore) or the [documentation index](https://fal.ai/docs/llms.txt).
3. Fetch that model's `llms.txt` for the exact schema, then call it using the [client setup](/docs/documentation/model-apis/inference/client-setup) guide.