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

# Coding Agents

> Teach your AI coding agent how to use Decart's APIs

Two ways to give your AI coding agent Decart expertise: **skills** teach it API patterns upfront, and the **MCP server** lets it search Decart's documentation on demand. Use both for best results.

## Agent skill

A skill is a static knowledge file your agent loads at startup. It teaches the agent which API to use, how to authenticate, which models to pick, and what mistakes to avoid.

<Steps>
  <Step title="Install the Decart skill">
    Run this in your project directory:

    ```bash theme={null}
    npx skills add https://docs.platform.decart.ai
    ```

    The CLI auto-discovers the skill and installs it to your detected coding agents.
  </Step>

  <Step title="Start building">
    Your agent now has context about Decart's Realtime, Queue, and Process APIs. Try asking it:

    * "Add live camera effects using Decart's realtime API"
    * "Generate a video from a text prompt using Decart"
    * "Create a talking avatar that lip-syncs to audio"
  </Step>
</Steps>

| Your agent learns | Details                                                                   |
| ----------------- | ------------------------------------------------------------------------- |
| API selection     | Which API (Realtime, Queue, Process) fits your use case                   |
| Model selection   | Which model for character transform, style transfer, video gen, image gen |
| Authentication    | Server-side API keys vs client-side token generation                      |
| SDK patterns      | Correct code patterns for JavaScript and Python                           |
| Common mistakes   | Camera resolution, API key exposure, cleanup, moderation handling         |

<AccordionGroup>
  <Accordion title="Install to a specific agent">
    ```bash theme={null}
    npx skills add https://docs.platform.decart.ai -a claude-code
    npx skills add https://docs.platform.decart.ai -a cursor
    ```
  </Accordion>

  <Accordion title="Install globally (all projects)">
    ```bash theme={null}
    npx skills add https://docs.platform.decart.ai -g
    ```
  </Accordion>
</AccordionGroup>

<Note>
  Skills work with 30+ coding agents including Claude Code, Cursor, GitHub Copilot, Windsurf, and OpenCode. See the [full list](https://skills.sh).
</Note>

## MCP server

The MCP (Model Context Protocol) server gives your agent a live search tool that queries Decart documentation on demand. Unlike the skill which is loaded once, MCP lets the agent look up specific details — exact parameters, migration guides, changelog entries — as it works.

Add the Decart MCP server to your agent:

<Tabs>
  <Tab title="Claude Code">
    ```bash theme={null}
    claude mcp add decart-docs https://docs.platform.decart.ai/mcp --transport http
    ```
  </Tab>

  <Tab title="Codex">
    Add to your `~/.codex/config.toml`:

    ```toml theme={null}
    [mcp_servers.decart-docs]
    type = "http"
    url = "https://docs.platform.decart.ai/mcp"
    ```
  </Tab>

  <Tab title="Cursor">
    Add to your `.cursor/mcp.json`:

    ```json theme={null}
    {
      "mcpServers": {
        "decart-docs": {
          "url": "https://docs.platform.decart.ai/mcp"
        }
      }
    }
    ```
  </Tab>
</Tabs>

Once connected, your agent can search Decart documentation automatically while generating responses — no need to ask it explicitly.

## Skill vs MCP

|                  | Skill                                  | MCP                                        |
| ---------------- | -------------------------------------- | ------------------------------------------ |
| **How it works** | Pre-loaded context at startup          | Live search during conversation            |
| **Best for**     | API patterns, model selection, gotchas | Exact parameters, examples, latest changes |
| **Setup**        | `npx skills add` (one time)            | Add MCP config (one time)                  |
| **Token cost**   | Loaded once (\~200 lines)              | Per-search, only what's needed             |

<Tip>
  Use both together. The skill gives your agent the big picture so it picks the right API and model. The MCP server fills in specifics when the agent needs exact parameter details or code examples.
</Tip>

## Prompting tips

Get better results from your agent by being specific about what you're building:

* **Name the API** — "Use the Realtime API" or "Use the Queue API" instead of just "use Decart"
* **Specify the model** — "Use `lucy-2.1` for character transform" rather than letting the agent guess
* **Mention the SDK** — "Using the JavaScript SDK" or "Using Python" so it generates the right syntax
* **Ask for client tokens** — "Make sure to use client tokens for the browser code" for frontend work
* **Reference the platform** — "Check the Decart docs for the exact parameters" triggers the MCP search