Docling MCP MCP Server

<p align="center"> <a href="https://github.com/docling-project/docling-mcp"> <img loading="lazy" alt="Docling" src="https://github.com/docling-project/docling-mcp/raw/main/docs/assets/docling_mcp.png" width="40%"/> </a> </p>

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.

Compatibility

docling-mcp	MCP Python SDK
>=3.0.0	mcp>=2.0.0
>=2.0.0,<3.0.0	mcp>=1.9.4,<2.0.0

If your MCP client application has not yet migrated to MCP SDK v2, pin:

pip install "docling-mcp<3.0.0"

See MIGRATION.md for the full migration guide.

Installation Options

Remote Mode (Recommended - Lightweight)

For users with access to Docling Serve API:

Getting Docling Serve: Visit docling-serve for installation guides. You can deploy it from published container images or look for managed Docling SaaS offerings.

pip install docling-mcp

Then configure your environment:

export DOCLING_MCP_SERVICE_URL=https://your-docling-service.example.com
export DOCLING_MCP_SERVICE_API_KEY=your-api-key-here
export DOCLING_MCP_CONVERSION_MODE=remote

Local Mode (Full Features)

For users who need local conversion or don't have Docling Serve access:

pip install docling-mcp[local]

Then configure your environment:

export DOCLING_MCP_CONVERSION_MODE=local

Hybrid Mode (Best of Both)

Install with local support and enable automatic fallback:

pip install docling-mcp[local]

Configure for remote with fallback:

export DOCLING_MCP_SERVICE_URL=https://your-docling-service.example.com
export DOCLING_MCP_CONVERSION_MODE=remote
export DOCLING_MCP_FALLBACK_TO_LOCAL=true

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

Configuration

All settings use the DOCLING_MCP_ prefix and can be supplied as environment variables, in a .env file in the working directory, or via the env block of your MCP client config. Copy .env.example as a starting point.

Conversion mode

Variable	Default	Description
DOCLING_MCP_CONVERSION_MODE	remote	remote or local

Remote service (required when DOCLING_MCP_CONVERSION_MODE=remote)

Variable	Default	Description
DOCLING_MCP_SERVICE_URL	—	URL of the Docling Serve instance
DOCLING_MCP_SERVICE_API_KEY	—	API key for the service
DOCLING_MCP_SERVICE_TIMEOUT	300.0	Request timeout in seconds
DOCLING_MCP_SERVICE_MAX_RETRIES	3	Max retry attempts
DOCLING_MCP_FALLBACK_TO_LOCAL	false	Fall back to local if service is unreachable (requires docling-mcp[local])

Conversion pipeline (applies to both modes)

Variable	Default	Description
DOCLING_MCP_KEEP_IMAGES	false	Retain page images in output
DOCLING_MCP_IMAGES_SCALE	1.0	Image scale factor (increase to avoid tensor padding errors)
DOCLING_MCP_DO_OCR	true	Run OCR pipeline
DOCLING_MCP_DO_TABLE_STRUCTURE	true	Detect table structure

LlamaIndex RAG (--tools llama-index-rag)

Variable	Default	Description
DOCLING_MCP_LI_API_BASE	http://127.0.0.1:1234/v1	OpenAI-compatible LLM endpoint
DOCLING_MCP_LI_API_KEY	none	API key for the LLM endpoint
DOCLING_MCP_LI_MODEL_ID	ibm/granite-3.2-8b	LLM model identifier
DOCLING_MCP_LI_EMBEDDING_MODEL	BAAI/bge-base-en-v1.5	HuggingFace embedding model

LlamaStack (--tools llama-stack-rag / --tools llama-stack-ie)

Variable	Default	Description
DOCLING_MCP_LLS_URL	http://localhost:8321	LlamaStack server URL
DOCLING_MCP_LLS_VDB_EMBEDDING	all-MiniLM-L6-v2	Embedding model for vector DB
DOCLING_MCP_LLS_EXTRACTION_MODEL	openai/gpt-oss-20b	Model used for structured extraction

Setting variables in an MCP client config

{
  "mcpServers": {
    "docling": {
      "command": "uvx",
      "args": [
        "--from=docling-mcp",
        "docling-mcp-server"
      ],
      "env": {
        "DOCLING_MCP_CONVERSION_MODE": "remote",
        "DOCLING_MCP_SERVICE_URL": "https://your-docling-service.example.com",
        "DOCLING_MCP_SERVICE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Getting started

The easiest way to install Docling MCP and connect it to your client is by 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 Developing section of CONTRIBUTING.md 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 click on the button below for a direct install.

Other integrations are described in the integrations page.

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.

Contributing

We welcome external contributions. See CONTRIBUTING.md for details on how to get started.

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.

<!-- MCP registry ownership marker (proves this PyPI package maps to the server name). --> <!-- mcp-name: io.github.docling-project/docling-mcp -->