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.

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 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.

Parameter	Description	Default
`url`	Document URL (required)	-
`model`	`pipeline` (fast) or `vlm` (accurate)	pipeline
`pages`	Page ranges to parse (e.g. `"1-10,15"`)	all pages
`formats`	Extra export formats beyond markdown	-
`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 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.

Parameter	Description	Default
`task_id`	Task ID from a parse request (required)	-
`format`	`concise` or `detailed` response	concise

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.

Parameter	Description	Default
`batch_id`	Batch ID from a batch request (required)	-
`limit`	Number of results to return	-
`offset`	Pagination offset	0
`format`	`concise` or `detailed` response	concise

5. `mineru_upload_batch`

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

Parameter	Description	Default
`directory`	Path to a folder of documents	-
`files`	Array of specific file paths	-
`model`	`pipeline` (fast) or `vlm` (accurate)	pipeline
`formula`	Recognize formulas	false
`table`	Detect and extract tables	true
`language`	OCR language	`en`
`formats`	Extra export formats	-

Provide either directory or files (not both).

6. `mineru_download_results`

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

Parameter	Description	Default
`batch_id`	Batch ID to download results for (required)	-
`output_dir`	Local directory for output files (required)	-
`overwrite`	Overwrite existing files	false

Local File Workflow: Tools 5 and 6 enable a complete local pipeline — upload files from your machine with mineru_upload_batch, poll with mineru_batch_status, then save results with mineru_download_results. No URLs or manual downloads needed.

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. 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

Visit mineru.net
Create account and generate API key
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

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

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

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"
      }
    }
  }
}

Add to your MCP settings JSON (VS Code uses servers, Cursor uses mcpServers):

{
  "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

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

MinerU MCP supports 11+ client configurations including Windsurf, Cline, Cherry Studio, and Witsy. See the full setup guide on GitHub for all options.

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
Formats	PDF, DOC, DOCX, PPT, PPTX, images	PDF only
Batch limit	200 docs	Unlimited
VLM mode	Yes (90%+)	No
Local files	Yes (upload_batch)	Yes
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 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

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.

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The Future of Research

Mastering MCP Servers

Research Plugins

Research Platforms

MinerU MCP: AI-Powered Document Parsing

What is MinerU MCP?

The Six Tools

1. `mineru_parse`

2. `mineru_status`

3. `mineru_batch`

4. `mineru_batch_status`

5. `mineru_upload_batch`

6. `mineru_download_results`

VLM Mode vs Pipeline Mode

Use Cases for Research

1. SLR Corpus Preparation

2. Local File Processing

3. Batch Processing for Literature Analysis

4. 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

Research Platforms

​What is MinerU MCP?

​The Six Tools

​1. mineru_parse

​2. mineru_status

​3. mineru_batch

​4. mineru_batch_status

​5. mineru_upload_batch

​6. mineru_download_results

​VLM Mode vs Pipeline Mode

​Use Cases for Research

​1. SLR Corpus Preparation

​2. Local File Processing

​3. Batch Processing for Literature Analysis

​4. 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 Six Tools

1. `mineru_parse`

2. `mineru_status`

3. `mineru_batch`

4. `mineru_batch_status`

5. `mineru_upload_batch`

6. `mineru_download_results`

VLM Mode vs Pipeline Mode

Use Cases for Research

1. SLR Corpus Preparation

2. Local File Processing

3. Batch Processing for Literature Analysis

4. 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