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Docling MCP

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Making Docling agentic through MCP

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★ 721 Stars Category · Other Very popular Source revision a3914dcb8774

50FMRS · D

Reliability

6/20

Security and permissions

10/20

Maintenance

10/20

Documentation

12/20

Setup experience

12/20

Docling MCP is a feature-rich document conversion server supporting remote and local modes, offering a wide range of tools (conversion, generation, RAG). It suits developers integrating document processing into AI agents, but configuration can be complex and remote mode depends on an external service.

Read the FMRS scoring method →

Docling MCP is a document processing service using the Model Context Protocol (MCP) for tool integration. It leverages the Docling library to convert PDF and other documents into structured formats, providing conversion, generation, and RAG tools. Supports remote (Docling Serve) and local (docling-mcp[local]) conversion modes.

Tools

convert_pdf_to_docling_document

Convert a local PDF document to structured DoclingDocument format.

convert_pdf_to_docling_document_from_url

Convert a PDF from a remote URL to DoclingDocument format.

create_new_docling_document

Create a new empty DoclingDocument.

add_title_to_docling_document

Add a title to the document.

add_heading_to_docling_document

Add a section heading to the document.

add_paragraph_to_docling_document

Add a paragraph to the document.

open_list_in_docling_document

Open a list within the document.

add_listitem_to_list_in_docling_document

Add a list item to the currently opened list.

close_list_in_docling_document

Close the currently opened list.

add_table_to_docling_document

Add a table to the document.

export_docling_document_to_markdown

Export the document to Markdown format.

export_docling_document_to_html

Export the document to HTML format.

save_docling_document

Save the document to a file.

retrieve_docling_document

Retrieve a document by its document key from cache.

convert_to_markdown

Convert a PDF directly to Markdown format.

milvus_upload

Upload a document to Milvus vector database.

milvus_search

Perform retrieval in Milvus.

llama_index_rag_search

Perform RAG retrieval using LlamaIndex.

Setup

Install the package: pip install docling-mcp or pip install docling-mcp[local].
Configure environment variables as needed (e.g., DOCLING_MCP_CONVERSION_MODE, DOCLING_MCP_SERVICE_URL, etc.).
Start the server: uvx --from docling-mcp docling-mcp-server --transport stdio.

claude_desktop_config.json

{
  "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"
      }
    }
  }
}

Generate and validate client config

Fit and risk

Best for

Developers looking to integrate document conversion into AI agents or MCP clients like Claude Desktop.
Teams needing PDF-to-Markdown/JSON conversion for RAG or knowledge management.
Users with access to Docling Serve or wanting hybrid mode with local fallback.

Not for

Pure document conversion users who do not need MCP integration (can use Docling directly).
Scenarios without any AI agent or MCP client.
Lightweight single-shot conversion with no caching requirements.

Required permissions

Read local files (access PDFs for conversion).
Network access (required for remote mode; optional for local mode).
Environment variables (must set DOCLING_MCP_* variables).
Storage permissions (for saving generated documents or caching).

Risks and side effects

Remote mode depends on Docling Serve; unavailability or high latency can cause failures.
API keys may be abused if leaked; handle with care.
Local conversion mode relies on system resources; large documents may cause high memory usage.
OCR and table structure detection may produce inaccurate results; verify outputs.

Troubleshooting

Check that `DOCLING_MCP_CONVERSION_MODE` is 'remote' and `DOCLING_MCP_SERVICE_URL` is correct.
Ensure the API key is valid and has permissions.
For remote mode, verify network connectivity and firewall settings.
If encountering tensor batching errors, try increasing `DOCLING_MCP_IMAGES_SCALE`.
Check server logs for detailed error messages.

Use cases

Convert PDF documents to structured JSON for AI applications.

Create, edit, and export Docling documents via AI agents.

Embed documents into vector databases (e.g., Milvus) for RAG pipelines.

Provide local or remote document conversion for LLMs.

Supported clients

Claude DesktopFull support

LM StudioFull support

Llama StackFull support

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