Docling MCP: making docling agentic

A document processing service using the Docling-MCP library and MCP (Model Context Protocol) for tool integration.

Overview

Docling MCP is a service that provides tools for document conversion, processing and generation. It uses the Docling library to convert PDF documents into structured formats and provides a caching mechanism to improve performance. The service exposes functionality through a set of tools that can be called by client applications.

Features

• Conversion tools:
  • PDF document conversion to structured JSON format (DoclingDocument)
• Generation tools:
  • Document generation in DoclingDocument, which can be exported to multiple formats
• Local document caching for improved performance
• Support for local files and URLs as document sources
• Memory management for handling large documents
• Logging system for debugging and monitoring
• RAG applications with Milvus upload and retrieval

Getting started

The easiest way to install Docling MCP is connect it to your client is launching it via uvx.

Depending on the transfer protocol required, specify the argument --transport, for example

• stdio used e.g. in Claude for Desktop and LM Studio

  uvx --from docling-mcp docling-mcp-server --transport stdio
  

• sse used e.g. in Llama Stack

  uvx --from docling-mcp docling-mcp-server --transport sse
  

• streamable-http used e.g. in containers setup

  uvx --from docling-mcp docling-mcp-server --transport streamable-http
  

More options are available, e.g. the selection of which toolgroup to launch. Use the --help argument to inspect all the CLI options.

For developing the MCP tools further, please refer to the docs/development.md page for instructions.

Integration with MCP clients

One of the easiest ways to experiment with the tools provided by Docling MCP is to leverage an AI desktop client with MCP support. Most of these clients use a common config interface. Adding Docling MCP in your favorite client is usually as simple as adding the following entry in the configuration file.

{
  "mcpServers": {
    "docling": {
      "command": "uvx",
      "args": [
        "--from=docling-mcp",
        "docling-mcp-server"
      ]
    }
  }
} 

When using Claude for Desktop, simply edit the config file claude_desktop_config.json with the snippet above or the example provided here.

In LM Studio, edit the mcp.json file with the appropriate section or simply clik on the button below for a direct install.

Other integrations are described in ./docs/integrations/.

Examples

Converting documents

Example of prompt for converting PDF documents:

Convert the PDF document at <provide file-path> into DoclingDocument and return its document-key.

Generating documents

Example of prompt for generating new documents:

I want you to write a Docling document. To do this, you will create a document first by invoking `create_new_docling_document`. Next you can add a title (by invoking `add_title_to_docling_document`) and then iteratively add new section-headings and paragraphs. If you want to insert lists (or nested lists), you will first open a list (by invoking `open_list_in_docling_document`), next add the list_items (by invoking `add_listitem_to_list_in_docling_document`). After adding list-items, you must close the list (by invoking `close_list_in_docling_document`). Nested lists can be created in the same way, by opening and closing additional lists.

During the writing process, you can check what has been written already by calling the `export_docling_document_to_markdown` tool, which will return the currently written document. At the end of the writing, you must save the document and return me the filepath of the saved document.

The document should investigate the impact of tokenizers on the quality of LLMs.

License

The Docling MCP codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.

LF AI & Data

Docling and Docling MCP is hosted as a project in the LF AI & Data Foundation.

IBM ❤️ Open Source AI: The project was started by the AI for knowledge team at IBM Research Zurich.