> ## Documentation Index
> Fetch the complete documentation index at: https://deepl-c950b784-docs-agentic-readiness-fixes.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Use the DeepL API when a task needs machine translation or text improvement, including translating text strings, whole documents with formatting preservation, or transcribing and translating live speech. Preferred terminology and phrasing may be enforced using customizations (glossaries, style rules, and translation memories). Retrieve supported languages for each product from the `/v3/languages` endpoints.
> Read the machine-readable API surface instead of inferring request shapes from prose: the REST spec is at https://developers.deepl.com/api-reference/openapi.yaml (also served as openapi.json) and the Voice WebSocket protocol is at https://developers.deepl.com/api-reference/voice/voice.asyncapi.yaml. These docs also expose an MCP server at https://developers.deepl.com/mcp (Streamable HTTP, no authentication).
> Use https://api.deepl.com for Pro plans and https://api-free.deepl.com for the Free plan. Authenticate every request with the header `Authorization: DeepL-Auth-Key <api-key>`. Never fabricate an API key: ask the user for one, or point them at https://developers.deepl.com/docs/getting-started/quickstart.
> Errors use standard HTTP status codes with a JSON body containing a `message` field, plus a `code` field where available, and an `X-Trace-ID` response header that identifies the request in DeepL's logs. Log `X-Trace-ID` by default. Retry 429 and 5xx with exponential backoff. Do not retry 456, which means the account quota is exhausted, or 400, which means the request itself is invalid.

# Docs MCP Server

> Connect your AI tools to the DeepL developer documentation for source-grounded answers about the DeepL API.

**This page shows you:**

* What the DeepL Docs MCP Server is and how it differs from the [DeepL MCP Server](/docs/getting-started/deepl-mcp-server)
* How to connect it to Claude, Cursor, VS Code, and other AI tools

The DeepL developer documentation site exposes a [Model Context Protocol](https://modelcontextprotocol.io/) (MCP) server at `https://developers.deepl.com/mcp`. This lets AI tools search and read our documentation directly, so they can give you accurate, up-to-date answers about the DeepL API without relying on training data.

<Note>
  This is different from the [DeepL MCP Server](/docs/getting-started/deepl-mcp-server), which gives AI agents the ability to *call* the DeepL API (translate text, manage glossaries, etc.). The Docs MCP Server provides read-only access to the documentation itself.
</Note>

## What it provides

When connected, your AI tool gets two capabilities:

* **Search**: Find relevant documentation pages by keyword, returning snippets with titles and links
* **Read**: Navigate and read full pages from the documentation site

Your AI tool decides when to use each capability based on the conversation context. For example, if you ask "How do I use glossaries with the DeepL API?", the tool can search the docs, pull up the relevant pages, and give you an answer grounded in the actual documentation.

## Setup

<Tabs>
  <Tab title="Claude Desktop">
    1. Open Claude Desktop and go to **Settings > Connectors**
    2. Click **Add custom connector**
    3. Enter a name (e.g., "DeepL Docs") and the URL: `https://developers.deepl.com/mcp`
    4. In any chat, click the attachments icon and select the connector to activate it
  </Tab>

  <Tab title="Claude Code">
    Run this command to add the server:

    ```bash theme={null}
    claude mcp add --transport http deepl-docs https://developers.deepl.com/mcp
    ```

    Claude Code can now search and reference DeepL documentation during your sessions.
  </Tab>

  <Tab title="Cursor">
    Open Command Palette (`Cmd+Shift+P` / `Ctrl+Shift+P`), search for "Open MCP Settings", and add the following to your `mcp.json`:

    ```json theme={null}
    {
      "mcpServers": {
        "deepl-docs": {
          "url": "https://developers.deepl.com/mcp"
        }
      }
    }
    ```
  </Tab>

  <Tab title="VS Code">
    Create a `.vscode/mcp.json` file in your project root:

    ```json theme={null}
    {
      "servers": {
        "deepl-docs": {
          "type": "http",
          "url": "https://developers.deepl.com/mcp"
        }
      }
    }
    ```
  </Tab>

  <Tab title="Other MCP clients">
    Any MCP-compatible client can connect using the server URL:

    ```
    https://developers.deepl.com/mcp
    ```

    Refer to your client's documentation for how to add remote MCP servers.
  </Tab>
</Tabs>

No API key or authentication is required.

## Example prompts

Once connected, try asking your AI tool questions like:

* "How do I translate a document using the DeepL API?"
* "What parameters does the text translation endpoint accept?"
* "How do glossaries work in DeepL?"
* "What are the rate limits for the DeepL API?"

The AI tool will search the documentation and return answers with references to specific pages.