Metadata-Version: 2.4
Name: docling-mcp
Version: 1.0.0
Summary: Running Docling as an agent using tools
Project-URL: Homepage, https://github.com/docling-project/docling-mcp
Project-URL: Repository, https://github.com/docling-project/docling-mcp
Project-URL: Issues, https://github.com/docling-project/docling-mcp/issues
Project-URL: Changelog, https://github.com/docling-project/docling-mcp/blob/main/CHANGELOG.md
Author-email: Peter Staar <taa@zurich.ibm.com>, Adel Zaalouk <azaalouk@redhat.com>, Michele Dolfi <dol@zurich.ibm.com>, Panos Vagenas <pva@zurich.ibm.com>, Christoph Auer <cau@zurich.ibm.com>, Cesar Berrospi Ramis <ceb@zurich.ibm.com>
Maintainer-email: Peter Staar <taa@zurich.ibm.com>, Adel Zaalouk <azaalouk@redhat.com>, Michele Dolfi <dol@zurich.ibm.com>, Panos Vagenas <pva@zurich.ibm.com>, Christoph Auer <cau@zurich.ibm.com>, Cesar Berrospi Ramis <ceb@zurich.ibm.com>
License-Expression: MIT
License-File: LICENSE
Keywords: AI,Docling,RAG,agentic,agents,artificial intelligence,document understanding,mcp,message control protocol
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Build Tools
Classifier: Typing :: Typed
Requires-Python: >=3.10
Requires-Dist: docling~=2.25
Requires-Dist: httpx>=0.28.1
Requires-Dist: mcp[cli]>=1.9.4
Requires-Dist: pydantic-settings~=2.4
Requires-Dist: pydantic~=2.10
Requires-Dist: python-dotenv>=1.1.0
Provides-Extra: llama-index-rag
Requires-Dist: llama-index-core>=0.12.28; extra == 'llama-index-rag'
Requires-Dist: llama-index-embeddings-huggingface>=0.5.2; extra == 'llama-index-rag'
Requires-Dist: llama-index-embeddings-openai>=0.3.1; extra == 'llama-index-rag'
Requires-Dist: llama-index-llms-ollama>=0.5.4; extra == 'llama-index-rag'
Requires-Dist: llama-index-node-parser-docling>=0.3.1; extra == 'llama-index-rag'
Requires-Dist: llama-index-readers-docling>=0.3.2; extra == 'llama-index-rag'
Requires-Dist: llama-index-readers-file>=0.4.7; extra == 'llama-index-rag'
Requires-Dist: llama-index-vector-stores-milvus>=0.7.2; extra == 'llama-index-rag'
Requires-Dist: llama-index>=0.12.33; extra == 'llama-index-rag'
Provides-Extra: llama-stack
Requires-Dist: fire>=0.7.0; extra == 'llama-stack'
Requires-Dist: llama-stack-client>=0.2.12; extra == 'llama-stack'
Description-Content-Type: text/markdown

<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 

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A document processing service using the Docling-MCP library and MCP (Message Control 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](https://docs.astral.sh/uv/).

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 [docs/development.md](docs/development.md) page for instructions.

## Integration with Claude for Desktop

One of the easiest ways to experiment with the tools provided by Docling-MCP is to leverage [Claude for Desktop](https://claude.ai/download).
Once installed, extend Claude for Desktop so that it can read from your computer’s file system, by following the [For Claude Desktop Users](https://modelcontextprotocol.io/quickstart/user) tutorial.

To enable Claude for Desktop with Docling MCP, simply edit the config file `claude_desktop_config.json` (located at `~/Library/Application Support/Claude/claude_desktop_config.json` in MacOS) and add a new item in the `mcpServers` key with the details of a Docling MCP server. You can find an example of those details [here](docs/integrations/claude_desktop_config.json).


## Examples

### Converting documents

Example of prompt for converting PDF documents:

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

### Generating documents

Example of prompt for generating new documents:

```prompt
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](https://lfaidata.foundation/projects/).

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

[docling_document]: https://docling-project.github.io/docling/concepts/docling_document/
[integrations]: https://docling-project.github.io/docling-mcp/integrations/