Docling MCP

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

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

Remote service (required when DOCLING_MCP_CONVERSION_MODE=remote)

Conversion pipeline (applies to both modes)

[!TIP]

Enabling DOCLING_MCP_DO_PICTURE_DESCRIPTION=true adds a VLM inference step for every figure in the document. On resource-constrained hardware (e.g. a laptop running the model on CPU) this can significantly increase conversion time.

- Remote mode — increase DOCLING_MCP_SERVICE_TIMEOUT (in seconds) to give the remote service enough time to complete. The default is 300.0 (5 minutes).

- Local mode / MCP client timeout — the MCP client itself may also enforce a request timeout. For example, in LM Studio set the timeout field (in milliseconds) in your mcp.json:

`json

{

"mcpServers": {

"docling": {

"url": "http://localhost:8000/mcp",

"timeout": 300000

}

}

}

`

Markdown export

Cache

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

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

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

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

• `sse` used e.g. in Llama Stack

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

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

sh 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](https://claude.ai/download), simply edit the config file claude_desktop_config.json with the snippet above or the example provided here.

In [LM Studio](https://lmstudio.ai/), 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 i