Related MCP Server Resources

Explore more AI models, providers, and integration options:

  • Explore AI Models
  • Explore AI Providers
  • Explore MCP Servers
  • LangDB Pricing
  • Documentation
  • AI Industry Blog
  • AWS Knowledge Base Retrieval MCP Server
  • ERPNext MCP Server
  • YOKATLAS API MCP Server
  • Perplexity AI MCP Server
  • Kintone MCP Server
Back to MCP Servers
MCP Docling Server

MCP Docling Server

Public
zanetworker/mcp-docling

Provides document processing capabilities via Model Context Protocol, including document conversion to markdown, OCR-enabled extraction, table parsing, batch processing, Q&A generation with IBM Watson X integration, and system info retrieval for seamless LLM application integration.

python
0 tools
May 29, 2025
Updated Jun 4, 2025

Supercharge Your AI with MCP Docling Server

MCP Server

Unlock the full potential of MCP Docling Server through LangDB's AI Gateway. Get enterprise-grade security, analytics, and seamless integration with zero configuration.

Unified API Access
Complete Tracing
Instant Setup
Get Started Now

Free tier available • No credit card required

Instant Setup
99.9% Uptime
10,000+Monthly Requests

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:

  1. 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)
  2. 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)
  3. extract_tables: Extract tables from a document as structured data

    • source: URL or local file path to the document (required)
  4. 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)
  5. 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)
  6. 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.

Publicly Shared Threads0

Discover shared experiences

Shared threads will appear here, showcasing real-world applications and insights from the community. Check back soon for updates!

Share your threads to help others
Related MCPs5
  • AWS Knowledge Base Retrieval MCP Server
    AWS Knowledge Base Retrieval MCP Server

    Retrieval-Augmented Generation (RAG) server enabling efficient extraction of contextual information ...

    Added May 30, 2025
  • ERPNext MCP Server
    ERPNext MCP Server

    Model Context Protocol server enabling seamless integration with ERPNext via API, offering authentic...

    Added May 30, 2025
  • YOKATLAS API MCP Server
    YOKATLAS API MCP Server

    Provides standardized Model Context Protocol (MCP) access to YÖKATLAS data, enabling programmatic se...

    Added May 30, 2025
  • Perplexity AI MCP Server
    Perplexity AI MCP Server

    Provides seamless integration with Perplexity AI via Model Context Protocol, enabling chat, search, ...

    5 tools
    Added May 30, 2025
  • Kintone MCP Server
    Kintone MCP Server

    Enables seamless integration with kintone via Model Context Protocol, offering comprehensive capabil...

    25 tools
    Added May 30, 2025