MCP Docling Server

An MCP server that provides document processing capabilities using the Docling library.

Installation

You can install the package using pip:

pip install -e .

Usage

Start the server using either stdio (default) or SSE transport:

# Using stdio transport (default)
mcp-server-lls

# Using SSE transport on custom port
mcp-server-lls --transport sse --port 8000

If you're using uv, you can run the server directly without installing:

# Using stdio transport (default)
uv run mcp-server-lls

# Using SSE transport on custom port
uv run mcp-server-lls --transport sse --port 8000

Available Tools

The server exposes the following tools:

convert_document: Convert a document from a URL or local path to markdown format
- source: URL or local file path to the document (required)
- enable_ocr: Whether to enable OCR for scanned documents (optional, default: false)
- ocr_language: List of language codes for OCR, e.g. ["en", "fr"] (optional)
convert_document_with_images: Convert a document and extract embedded images
- source: URL or local file path to the document (required)
- enable_ocr: Whether to enable OCR for scanned documents (optional, default: false)
- ocr_language: List of language codes for OCR (optional)
extract_tables: Extract tables from a document as structured data
- source: URL or local file path to the document (required)
convert_batch: Process multiple documents in batch mode
- sources: List of URLs or file paths to documents (required)
- enable_ocr: Whether to enable OCR for scanned documents (optional, default: false)
- ocr_language: List of language codes for OCR (optional)
qna_from_document: Create a Q&A document from a URL or local path to YAML format
- source: URL or local file path to the document (required)
- no_of_qnas: Number of expected Q&As (optional, default: 5)
- Note: This tool requires IBM Watson X credentials to be set as environment variables:
  - WATSONX_PROJECT_ID: Your Watson X project ID
  - WATSONX_APIKEY: Your IBM Cloud API key
  - WATSONX_URL: The Watson X API URL (default: https://us-south.ml.cloud.ibm.com)
get_system_info: Get information about system configuration and acceleration status

Example with Llama Stack

https://github.com/user-attachments/assets/8ad34e50-cbf7-4ec8-aedd-71c42a5de0a1

You can use this server with Llama Stack to provide document processing capabilities to your LLM applications. Make sure you have a running Llama Stack server, then configure your INFERENCE_MODEL

from llama_stack_client.lib.agents.agent import Agent
from llama_stack_client.lib.agents.event_logger import EventLogger
from llama_stack_client.types.agent_create_params import AgentConfig
from llama_stack_client.types.shared_params.url import URL
from llama_stack_client import LlamaStackClient
import os

# Set your model ID
model_id = os.environ["INFERENCE_MODEL"]
client = LlamaStackClient(
    base_url=f"http://localhost:{os.environ.get('LLAMA_STACK_PORT', '8080')}"
)

# Register MCP tools
client.toolgroups.register(
    toolgroup_id="mcp::docling",
    provider_id="model-context-protocol",
    mcp_endpoint=URL(uri="http://0.0.0.0:8000/sse"))

# Define an agent with MCP toolgroup
agent_config = AgentConfig(
    model=model_id,
    instructions="""You are a helpful assistant with access to tools to manipulate documents.
Always use the appropriate tool when asked to process documents.""",
    toolgroups=["mcp::docling"],
    tool_choice="auto",
    max_tool_calls=3,
)

# Create the agent
agent = Agent(client, agent_config)

# Create a session
session_id = agent.create_session("test-session")

def _summary_and_qna(source: str):
    # Define the prompt
    run_turn(f"Please convert the document at {source} to markdown and summarize its content.")
    run_turn(f"Please generate a Q&A document with 3 items for source at {source} and display it in YAML format.")

def _run_turn(prompt):
    # Create a turn
    response = agent.create_turn(
        messages=[
            {
                "role": "user",
                "content": prompt,
            }
        ],
        session_id=session_id,
    )

    # Log the response
    for log in EventLogger().log(response):
        log.print()

_summary_and_qna('https://arxiv.org/pdf/2004.07606')

Caching

The server caches processed documents in ~/.cache/mcp-docling/ to improve performance for repeated requests.

Usage

Start the server using either stdio (default) or SSE transport:

# Using stdio transport (default) mcp-server-lls # Using SSE transport on custom port mcp-server-lls --transport sse --port 8000

If you're using uv, you can run the server directly without installing:

# Using stdio transport (default) uv run mcp-server-lls # Using SSE transport on custom port uv run mcp-server-lls --transport sse --port 8000

Available Tools

The server exposes the following tools:

convert_document: Convert a document from a URL or local path to markdown format

source: URL or local file path to the document (required)
enable_ocr: Whether to enable OCR for scanned documents (optional, default: false)
ocr_language: List of language codes for OCR, e.g. ["en", "fr"] (optional)

convert_document_with_images: Convert a document and extract embedded images

source: URL or local file path to the document (required)
enable_ocr: Whether to enable OCR for scanned documents (optional, default: false)
ocr_language: List of language codes for OCR (optional)

extract_tables: Extract tables from a document as structured data

source: URL or local file path to the document (required)

convert_batch: Process multiple documents in batch mode

sources: List of URLs or file paths to documents (required)
enable_ocr: Whether to enable OCR for scanned documents (optional, default: false)
ocr_language: List of language codes for OCR (optional)

qna_from_document: Create a Q&A document from a URL or local path to YAML format

source: URL or local file path to the document (required)
no_of_qnas: Number of expected Q&As (optional, default: 5)
Note: This tool requires IBM Watson X credentials to be set as environment variables:
- WATSONX_PROJECT_ID: Your Watson X project ID
- WATSONX_APIKEY: Your IBM Cloud API key
- WATSONX_URL: The Watson X API URL (default: https://us-south.ml.cloud.ibm.com)

get_system_info: Get information about system configuration and acceleration status

Example with Llama Stack

You can use this server with Llama Stack to provide document processing capabilities to your LLM applications. Make sure you have a running Llama Stack server, then configure your INFERENCE_MODEL

from llama_stack_client.lib.agents.agent import Agent from llama_stack_client.lib.agents.event_logger import EventLogger from llama_stack_client.types.agent_create_params import AgentConfig from llama_stack_client.types.shared_params.url import URL from llama_stack_client import LlamaStackClient import os # Set your model ID model_id = os.environ["INFERENCE_MODEL"] client = LlamaStackClient( base_url=f"http://localhost:{os.environ.get('LLAMA_STACK_PORT', '8080')}" ) # Register MCP tools client.toolgroups.register( toolgroup_id="mcp::docling", provider_id="model-context-protocol", mcp_endpoint=URL(uri="http://0.0.0.0:8000/sse")) # Define an agent with MCP toolgroup agent_config = AgentConfig( model=model_id, instructions="""You are a helpful assistant with access to tools to manipulate documents. Always use the appropriate tool when asked to process documents.""", toolgroups=["mcp::docling"], tool_choice="auto", max_tool_calls=3, ) # Create the agent agent = Agent(client, agent_config) # Create a session session_id = agent.create_session("test-session") def _summary_and_qna(source: str): # Define the prompt run_turn(f"Please convert the document at {source} to markdown and summarize its content.") run_turn(f"Please generate a Q&A document with 3 items for source at {source} and display it in YAML format.") def _run_turn(prompt): # Create a turn response = agent.create_turn( messages=[ { "role": "user", "content": prompt, } ], session_id=session_id, ) # Log the response for log in EventLogger().log(response): log.print() _summary_and_qna('https://arxiv.org/pdf/2004.07606')