# Use these docs in your agent (/docs/use-in-your-agent)

These docs are built to be read by agents as well as people. You can connect
them to a coding agent over MCP, feed the whole corpus to a model as plain
text, or copy any single page as Markdown. This page shows each option and the
exact config to paste.

<Callout title="Nothing to install">
  The MCP endpoint below is hosted by GitMCP and reads this public repository
  directly. There is no AISquare-hosted server to run and no credentials to
  manage. Point your client at the URL and you are done.
</Callout>

## Query the docs over MCP [#query-the-docs-over-mcp]

[GitMCP](https://gitmcp.io) turns this repository into a Model Context Protocol
server with no build step. It indexes the docs and exposes tools your agent can
call to search and fetch pages while it works. The endpoint is:

```text
https://gitmcp.io/AISquare-Studio/docs
```

### mcp.json [#mcpjson]

Most MCP clients read a JSON config that lists the servers they can reach. Add
this server to that file:

```json
{
  "mcpServers": {
    "aisquare-docs": {
      "url": "https://gitmcp.io/AISquare-Studio/docs"
    }
  }
}
```

Some clients still expect a command-based (stdio) server rather than a remote
URL. For those, run the remote endpoint through `mcp-remote`:

```json
{
  "mcpServers": {
    "aisquare-docs": {
      "command": "npx",
      "args": ["mcp-remote", "https://gitmcp.io/AISquare-Studio/docs"]
    }
  }
}
```

### Add to Cursor [#add-to-cursor]

Cursor reads MCP servers from `~/.cursor/mcp.json` (global) or `.cursor/mcp.json`
in a project. Add the server there:

```json
{
  "mcpServers": {
    "aisquare-docs": {
      "url": "https://gitmcp.io/AISquare-Studio/docs"
    }
  }
}
```

Reload Cursor, open Settings, and confirm `aisquare-docs` shows as connected
under MCP. Your agent can now search and fetch these docs as it answers.

<Callout type="info" title="Other clients">
  Claude Code, Claude Desktop, Windsurf, and VS Code read the same `mcpServers`
  shape. Drop the `aisquare-docs` block into that client's MCP config and point
  it at the same URL.
</Callout>

## Feed the whole corpus to a model [#feed-the-whole-corpus-to-a-model]

Two routes expose these docs as plain Markdown that any model can read in one
request. They follow the [llms.txt](https://llmstxt.org) convention.

<Cards>
  <Card title="/llms.txt" href="/llms.txt" description="A compact index: the site summary plus a linked map of every page. Best when a model should pick what to read." />

  <Card title="/llms-full.txt" href="/llms-full.txt" description="The full text of every page concatenated into one file. Best for pasting the entire corpus into a context window." />
</Cards>

Use `/llms.txt` when you want a small map and let the model follow links. Use
`/llms-full.txt` when you want everything in one payload, for example to ground
a one-off question or seed a retrieval index.

## Copy a single page [#copy-a-single-page]

Every page in these docs carries its own AI affordances in the header, next to
the title:

* **Copy Markdown** copies the page as clean Markdown, ready to paste into a
  chat or a prompt.
* **Open in ChatGPT, Claude, or Cursor** opens that assistant with the page
  already loaded as context.

You can also fetch the raw Markdown of any page directly. Append `.md` to its
URL:

```text
https://docs.aisquare.studio/docs/getting-started/quickstart.md
```

The same URL without the suffix returns Markdown too when the request sends an
`Accept: text/markdown` header, so a crawler or agent gets Markdown while a
browser gets the rendered page.

<Callout type="info" title="Questions">
  Building an agent on AISquare and need something these surfaces do not cover?
  Reach us at [bots@aisquare.studio](mailto:bots@aisquare.studio).
</Callout>