MinerU MCP: AI-Powered Document Parsing

MinerU MCP integrates MinerU's document parsing API directly into Claude, enabling AI-powered document analysis without leaving your research workflow.

What is MinerU MCP?

An MCP server that wraps MinerU's document parsing API, optimized for Claude Code research workflows. Instead of switching between tools or running scripts, you can parse documents directly within your AI conversation.

Why use MinerU MCP instead of manual conversion?

• Integrated workflow: Parse documents without leaving Claude
• Multi-format support: PDF, DOC, DOCX, PPT, PPTX, PNG, JPG, JPEG
• Batch processing: Handle 200 documents simultaneously
• Local file workflow: Upload files from your machine, poll for completion, download results
• Quality options: Choose speed (Pipeline) or accuracy (VLM)
• 73% token reduction: Optimized tool descriptions for efficient context usage

The Six Tools

MinerU MCP provides six tools covering two workflows: URL-based parsing (tools 1--4) and a local file pipeline (tools 5--6).

1. mineru_parse

Process a single document with customizable options.

Example prompt:

Parse pages 1-25 of this paper with VLM mode for maximum accuracy:
https://arxiv.org/pdf/2401.12345.pdf

2. mineru_status

Check task completion and get download URLs.

Example prompt:

Check the status of my parsing job and download the markdown when ready

3. mineru_batch

Process multiple document URLs simultaneously — perfect for SLR corpus preparation.

Limits:

• Maximum 200 documents per batch
• 200MB per file, 600 pages per document
• 2000 pages/day at high priority

Example prompt:

Batch process these 50 papers using VLM mode for my literature review:
[list of URLs]

4. mineru_batch_status

Retrieve paginated results from batch jobs.

5. mineru_upload_batch

Upload local files from your machine for batch processing — no need to host files at a URL.

Provide either directory or files (not both).

6. mineru_download_results

Download processed results as named markdown files to a local directory.

VLM Mode vs Pipeline Mode

Parse with model='vlm' for maximum accuracy

Quick parse this document for initial review

Use Cases for Research

1. SLR Corpus Preparation

Converting 50+ papers for systematic review:

I have 47 papers from my Scopus search that need to be converted
to markdown for analysis. Here are the URLs:
[paste URLs]

Use VLM mode for accurate table extraction. This is for my
systematic literature review on organizational learning.

2. Local File Processing

When your papers are already downloaded (e.g., from Zotero):

Upload all PDFs in ~/Documents/slr-papers/ using VLM mode,
then download the results to ~/Documents/slr-markdown/

3. Batch Processing for Literature Analysis

Screen a large set before detailed analysis:

Quick parse these 100 papers using pipeline mode to extract
abstracts and key sections. I'll do detailed VLM parsing
on the 20 most relevant ones later.

4. Multilingual Research

MinerU supports 109 OCR languages:

Parse this German-language paper with OCR enabled and
language set to 'de'. Extract the methodology section.

Installation & Setup

Step 1: Get API Key

1. Visit mineru.net
2. Create account and generate API key
3. Save securely (you'll need it for configuration)

Step 2: Install MCP

claude mcp add mineru-mcp -e MINERU_API_KEY=your-api-key -- npx -y mineru-mcp

codex mcp add mineru --env MINERU_API_KEY=your-api-key -- npx -y mineru-mcp

gemini mcp add -e MINERU_API_KEY=your-api-key mineru npx -y mineru-mcp

{
  "mcpServers": {
    "mineru": {
      "command": "npx",
      "args": ["-y", "mineru-mcp"],
      "env": {
        "MINERU_API_KEY": "your-api-key"
      }
    }
  }
}

{
  "mcpServers": {
    "mineru": {
      "command": "npx",
      "args": ["-y", "mineru-mcp"],
      "env": {
        "MINERU_API_KEY": "your-api-key"
      }
    }
  }
}

npx -y @smithery/cli install @linxule/mineru-mcp --client claude

Configuration Options

Integration with Research Memex

With OCR Guide

MinerU MCP is the recommended approach for PDF conversion in Research Memex workflows. See the PDF to Markdown Conversion Guide for comparison with other methods.

With SLR Workflow

Use MinerU for batch PDF processing in your Systematic Literature Review workflow. Perfect for converting your Zotero exports to AI-ready markdown.

With Interpretive Orchestration

MinerU is bundled as an optional MCP in the Interpretive Orchestration Plugin for qualitative research. It powers document ingestion alongside Markdownify for a complete document processing pipeline.

MinerU vs Mistral OCR

Recommendation: Use MinerU MCP for integrated Claude workflows and multi-format documents. Use Mistral script for very large offline batch jobs.

Limitations & Considerations

• API key required — Get from mineru.net
• File size: 200MB max per file
• Page limit: 600 pages per document
• Daily quota: 2000 pages at high priority
• VLM mode: More accurate but slower and costlier

Resources

• GitHub: linxule/mineru-mcp
• npm: mineru-mcp
• Smithery: Install for any AI client
• MinerU Platform: mineru.net
• MinerU Open Source: opendatalab/MinerU
• Related: OCR Guide | SLR Workflow