Research Memex - AI-Powered Research

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

Created by: Xule LinKey Stats: 90%+ accuracy (VLM mode) | 109 languages | Up to 200 documents per batch | 73% token reductionPerfect for: Systematic literature reviews, batch PDF processing, research corpus preparation

Part of the Ecosystem: MinerU is available as an optional MCP in the Interpretive Orchestration Plugin, powering high-accuracy PDF parsing for qualitative research workflows!

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 PDFs without leaving Claude
Batch processing: Handle 200 documents simultaneously
Quality options: Choose speed (Pipeline) or accuracy (VLM)
73% token reduction: Optimized tool descriptions for efficient context usage

The Four Tools

1. `mineru_parse`

Process a single document with customizable options.

Parameter	Description	Default
`url`	Document URL (required)	-
`model`	`pipeline` (fast) or `vlm` (accurate)	pipeline
`ocr`	Enable OCR for scanned documents	false
`formula`	Recognize mathematical/chemical formulas	false
`table`	Detect and extract tables	true
`language`	OCR language (109 supported)	`en`

Example prompt:

Parse 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 documents 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.

VLM Mode vs Pipeline Mode

VLM Mode
Pipeline Mode

Best for: Academic papers, complex layouts, tables, formulas

90%+ accuracy using Vision Language Models
Slower processing (worth the wait for important documents)
Higher API cost
Recommended for SLR corpus where accuracy matters

Parse with model='vlm' for maximum accuracy

Best for: Simple documents, speed priority, exploratory screening

Faster processing
Lower cost
Good for initial screening passes
Default mode for quick tasks

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. 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.

3. 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

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

Step 2: Install MCP

Claude Code
Smithery
Claude Desktop

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

Verify with claude mcp list - you should see mineru-mcp available.

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

Works with Claude Desktop, Cherry Studio, and other MCP clients. Set your API key in environment variables.

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

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

Configuration Options

Variable	Default	Purpose
`MINERU_API_KEY`	Required	Bearer token from mineru.net
`MINERU_BASE_URL`	`https://mineru.net/api/v4`	API endpoint
`MINERU_DEFAULT_MODEL`	`pipeline`	Default parsing mode

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

Feature	MinerU MCP	Mistral OCR (Script)
Integration	MCP (inline in Claude)	Python script
Best for	Claude workflows, real-time	Bulk offline processing
Batch limit	200 docs	Unlimited
VLM mode	Yes (90%+)	No
Languages	109	Variable
Setup	API key + MCP	API key + Python
Cost	Per-page API	Per-page API

Recommendation: Use MinerU MCP for integrated Claude workflows. 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
Smithery: Install for any AI client
MinerU Platform: mineru.net
MinerU Open Source: opendatalab/MinerU
Related: OCR Guide | SLR Workflow

Meta-Moment: This MCP was created to solve a real problem: batch processing PDFs for systematic literature reviews without context-switching. Now it’s documented in the same Research Memex that inspired its creation.The tools shape the methodology, and the methodology shapes the tools. 📄→🔄→📚

← Back to Advanced Topics

The Future of Research

Mastering MCP Servers

Research Plugins

MinerU MCP: AI-Powered Document Parsing

What is MinerU MCP?

The Four Tools

1. `mineru_parse`

2. `mineru_status`

3. `mineru_batch`

4. `mineru_batch_status`

VLM Mode vs Pipeline Mode

Use Cases for Research

1. SLR Corpus Preparation

2. Batch Processing for Literature Analysis

3. Multilingual Research

Installation & Setup

Step 1: Get API Key

Step 2: Install MCP

Configuration Options

Integration with Research Memex

With OCR Guide

With SLR Workflow

With Interpretive Orchestration

MinerU vs Mistral OCR

Limitations & Considerations

Resources

The Future of Research

Mastering MCP Servers

Research Plugins

​What is MinerU MCP?

​The Four Tools

​1. mineru_parse

​2. mineru_status

​3. mineru_batch

​4. mineru_batch_status

​VLM Mode vs Pipeline Mode

​Use Cases for Research

​1. SLR Corpus Preparation

​2. Batch Processing for Literature Analysis

​3. Multilingual Research

​Installation & Setup

​Step 1: Get API Key

​Step 2: Install MCP

​Configuration Options

​Integration with Research Memex

​With OCR Guide

​With SLR Workflow

​With Interpretive Orchestration

​MinerU vs Mistral OCR

​Limitations & Considerations

​Resources

What is MinerU MCP?

The Four Tools

1. `mineru_parse`

2. `mineru_status`

3. `mineru_batch`

4. `mineru_batch_status`

VLM Mode vs Pipeline Mode

Use Cases for Research

1. SLR Corpus Preparation

2. Batch Processing for Literature Analysis

3. Multilingual Research

Installation & Setup

Step 1: Get API Key

Step 2: Install MCP

Configuration Options

Integration with Research Memex

With OCR Guide

With SLR Workflow

With Interpretive Orchestration

MinerU vs Mistral OCR

Limitations & Considerations

Resources