Mineru Document Explorer

MinerU Document Explorer

Agent-native knowledge engine — hybrid search and deep reading over Markdown, PDF, DOCX, PPTX. Designed for AI agents to organize knowledge and retrieve information autonomously.

Quick Reference

Agent Principles

Follow these rules to use the tools effectively:

1. Collection-relative paths only. All file paths are prefixed by collection name: mydocs/readme.md, papers/survey.pdf. Never use absolute filesystem paths like /Users/.../file.md. You can also use qmd://mydocs/readme.md.

2. Navigate before reading large documents. For PDFs, DOCX, PPTX, or Markdown files >100 lines, always use doc_toc → doc_read instead of get. The get tool dumps the entire document — wasteful for large files.

3. Addresses bridge navigation and reading. doc_toc, doc_grep, and doc_query return address strings like line:45-120. Pass these directly to doc_read. Never call doc_read without addresses from one of these.

4. Use simple query first. Start with { "query": "your terms" }. Only switch to advanced searches mode when simple mode misses. The system auto-expands into BM25 + semantic + reranking.

5. Always pass source when writing wiki pages. This enables provenance tracking and staleness detection via wiki_lint.

6. Prefer MCP over CLI. The MCP server keeps models loaded in memory (~3GB). CLI reloads on every invocation (~5-15s overhead). If MCP is not available, qmd search (BM25 only) is instant and needs no model loading.

Key Concepts

Collections and File Paths

Documents live in collections — named groups with a filesystem path and glob mask. Collections have two types:

• raw (default) — immutable source documents, read-only for agents
• wiki — LLM-maintained pages, agents create/update via doc_write

File paths in all results are collection-relative: mydocs/readme.md, papers/survey.pdf. Use these exact paths when calling tools.

Document IDs (docid)

Every document has a short hash ID like #abc123 shown in search results. Use docids anywhere a file path is accepted: get("#abc123"), doc_toc("#abc123"). The # prefix is optional.

Addresses

Addresses identify locations within a document. They are the bridge between navigation tools (doc_toc, doc_grep, doc_query) and the reading tool (doc_read).

Three Tool Groups (15 tools)

Playbook 0: First-Run Setup & Configuration

Use when a user first connects MinerU Document Explorer, gives you the project link, or when PDF/DOCX/PPTX operations fail. Walk the user through setup interactively — check each prerequisite and guide them step by step.

Step 1 — Check qmd is installed

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which qmd && qmd status

If not installed:

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# Option A: npm (recommended)
npm install -g mineru-document-explorer

# Option B: from source
git clone https://github.com/opendatalab/MinerU-Document-Explorer.git
cd MinerU-Document-Explorer && bun install && bun link

Step 2 — Check Python for binary document support

PDF, DOCX, and PPTX processing requires Python 3.10+:

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python3 --version

If Python is missing, guide the user to install it for their platform:

• macOS: brew install python@3.12
• Ubuntu/Debian: sudo apt install python3 python3-pip
• Windows: Download from https://python.org

Step 3 — Check and install Python packages

Three packages are required for binary document processing:

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python3 -c "import pymupdf; import docx; import pptx; print('All dependencies OK')"

If any import fails, install the missing packages:

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pip install pymupdf python-docx python-pptx

Step 4 — Ask about advanced PDF processing (optional)

Ask the user: "Do you need high-quality PDF extraction for scanned documents or complex layouts? MinerU Cloud provides significantly better results than basic PyMuPDF."

If yes, guide them to set up MinerU Cloud:

1. Get an API key from https://mineru.net
2. Configure it (pick one method):

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# Method A: Environment variable
export MINERU_API_KEY="your-key-here"

# Method B: Config file (~/.config/qmd/doc-reading.json)
mkdir -p ~/.config/qmd
cat > ~/.config/qmd/doc-reading.json << 'EOF'
{
  "docReading": {
    "providers": {
      "fullText": { "pdf": ["mineru_cloud", "pymupdf"] }
    },
    "credentials": {
      "mineru": { "api_key": "YOUR_API_KEY_HERE" }
    }
  }
}
EOF

When MINERU_API_KEY is set, MinerU Cloud is automatically used as the primary PDF provider with PyMuPDF as fallback — no config file needed.

Additional Python package for MinerU Cloud:

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pip install mineru-open-sdk

Step 5 — Index documents and verify

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# Index a folder (adjust path to user's documents)
qmd collection add ~/Documents --name mydocs --mask '**/*.{md,pdf,docx,pptx}'

# Verify indexing worked
qmd status

# Test search (instant, no model downloads)
qmd search "test"

Step 6 — Configure MCP server (for AI agent integration)

Ask the user which AI client they use and provide the matching config:

Claude Code (~/.claude/settings.json):

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{ "mcpServers": { "qmd": { "command": "qmd", "args": ["mcp"] } } }

Cursor (.cursor/mcp.json) — HTTP mode recommended:

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qmd mcp --http --daemon   # start the server first

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{ "mcpServers": { "qmd": { "url": "http://localhost:8181/mcp" } } }

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

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{ "mcpServers": { "qmd": { "command": "qmd", "args": ["mcp"] } } }

Configuration Reference

Config file locations (later overrides earlier):

1. ~/.config/qmd/doc-reading.json — global settings
2. ./qmd.config.json — project-level overrides
3. Environment variables — highest priority

Full config example (~/.config/qmd/doc-reading.json):

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{
  "docReading": {
    "providers": {
      "fullText": { "pdf": ["mineru_cloud", "pymupdf"] },
      "toc":      { "pdf": ["native_bookmarks"] },
      "elements": { "docx": ["python_docx_local"], "pptx": ["python_pptx_local"] }
    },
    "credentials": {
      "mineru": {
        "api_key": "your-mineru-api-key",
        "api_url": "https://mineru.net/api/v4"
      },
      "openai": {
        "api_key": "your-openai-api-key",
        "base_url": "https://api.openai.com/v1"
      }
    }
  }
}

Environment variables:

Provider options:

Playbook 1: Search & Answer a Question

Use when the user asks a question and you need to find information.

Step 1 — Search:

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query({ "query": "how does authentication work" })

Results include docid, file, score, snippet. Use these to decide what to read.

Step 2 — Read the top result:

If the document is short (snippet suggests it's a small file):

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get({ "file": "#abc123" })

If the document is large or structured (PDF, long Markdown):

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doc_toc({ "file": "#abc123" })
doc_read({ "file": "#abc123", "addresses": ["line:11-20", "line:31-49"] })

Step 3 — Synthesize and answer using the content you read.

Tips:

• Add intent to disambiguate: { "query": "performance", "intent": "web page load times" }
• Use collections to narrow scope: { "query": "...", "collections": ["papers"] }
• The get response header includes Total lines: — if >100, switch to doc_toc + doc_read

Playbook 2: Deep-Read a Large Document

Use when the user points to a specific large document (PDF, DOCX, PPTX, or long Markdown) and wants to understand it.

Step 1 — Get the table of contents:

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doc_toc({ "file": "papers/survey.pdf" })

Returns a nested tree of sections with addresses. This is your map.

Step 2 — Read relevant sections:

Pick addresses from the TOC and read them:

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doc_read({ "file": "papers/survey.pdf", "addresses": ["line:11-20", "line:45-60"] })

Step 3 — Search within the document (if you need to find something specific):

For keywords:

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doc_grep({ "file": "papers/survey.pdf", "pattern": "attention mechanism" })

For concepts (semantic, requires embeddings):

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doc_query({ "file": "papers/survey.pdf", "query": "what evaluation metrics were used" })

Both return addresses — pass them to doc_read.

Step 4 — Extract structured elements (tables, figures):

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doc_elements({ "file": "report.pdf", "element_types": ["table"], "query": "revenue" })

Example flow:

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doc_toc("papers/survey.pdf")
  → sees section "3. Methodology" at line:45-80
doc_read("papers/survey.pdf", ["line:45-80"])
  → reads methodology section
doc_grep("papers/survey.pdf", "dataset")
  → finds mentions at line:62, line:78
doc_read("papers/survey.pdf", ["line:60-65", "line:76-80"])
  → reads specific paragraphs around dataset mentions

Playbook 3: Build Wiki from Sources

Use when the user wants to build a persistent knowledge base from their documents. Requires a wiki-type collection.

Step 1 — Ingest a source document:

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wiki_ingest({ "source": "mydocs/distributed-systems.md", "wiki_collection": "mywiki" })

Returns: source content, TOC, related existing wiki pages, and suggestions for what pages to create. Incremental — skips unchanged sources unless force: true.

Step 2 — Deep-read key sections (for large sources):

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doc_toc({ "file": "mydocs/distributed-systems.md" })
doc_read({ "file": "mydocs/distributed-systems.md", "addresses": ["line:11-30"] })

Step 3 — Write wiki pages:

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doc_write({
  "collection": "mywiki",
  "path": "concepts/cap-theorem.md",
  "content": "# CAP Theorem\n\n**Source:** [[sources/distributed-systems]]\n\n## Overview\n\nThe CAP theorem states that...\n\n## Connections\n- Related to [[concepts/consistency-models]]\n- See also [[concepts/consensus-algorithms]]",
  "title": "CAP Theorem",
  "source": "mydocs/distributed-systems.md"
})

Use [[wikilinks]] to create cross-references. Always pass source for provenance tracking.

Step 4 — Health-check:

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wiki_lint({ "collection": "mywiki", "stale_days": 30 })

Detects orphan pages, broken links, missing pages, stale content.

Wiki page template:

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# Page Title

**Source:** [[sources/paper-name]]

## Key Points
- ...

## Connections
- Related to [[concepts/topic-a]]
- Extends [[concepts/topic-b]]

Playbook 4: Maintain Wiki Health

Use periodically to keep the wiki knowledge base healthy.

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wiki_lint({ "collection": "mywiki" })

Act on the results:

• Orphan pages → add [[wikilinks]] from related pages
• Broken links → fix the link target or create the missing page
• Missing pages → create them with doc_write
• Stale pages → re-read the source with doc_read, update with doc_write

View activity history:

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wiki_log({ "since": "2025-01-01", "limit": 20 })

Generate or update the wiki index:

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wiki_index({ "collection": "mywiki", "write": true })

Tool Reference

Retrieval Tools

query — Search the knowledge base (primary search tool)

Simple mode auto-expands into BM25 + semantic + reranking. For advanced mode, first sub-query gets 2x weight.

get — Retrieve a single document

Response header includes Total lines: — if >100, prefer doc_toc + doc_read. On "not found" errors, check "Did you mean?" suggestions.

multi_get — Batch retrieve

Pattern examples: journals/2025-05*.md, readme.md, config.md, docs/api*.md, docs/config*.md, #abc123, #def456.

status — Index health (no parameters)

Returns document counts, embedding status, collection list. When connected via MCP, this info is already in the system prompt.

Deep Reading Tools

doc_toc — Table of contents

Returns a nested tree of sections with address fields. Start here for any large document.

doc_read — Read at addresses

doc_grep — Keyword search within a document

Returns matches with address fields for doc_read.

doc_query — Semantic search within a document

Returns ranked chunks with address fields. Requires embeddings.

doc_elements — Extract tables, figures, equations

doc_links — Forward/backward link graph

Knowledge Ingestion Tools

wiki_ingest — Prepare source for wiki processing

Returns source content, TOC, related pages, suggestions. Large docs (>50k chars) are truncated — use doc_read for details.

doc_write — Write a document

Writes to disk and immediately re-indexes. Wiki collections auto-log.

wiki_lint — Health check

wiki_log — Activity timeline

wiki_index — Generate index page

Decision Tree

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START
  │
  ├─ "What's indexed?" → status
  │
  ├─ "Find documents about X" → query
  │     Next: get (small docs) or doc_toc → doc_read (large docs)
  │
  ├─ "Get this specific file" → get (path or #docid)
  │     ⚠ For large docs: use doc_toc + doc_read instead
  │
  ├─ "Get several files" → multi_get (glob or comma-list)
  │
  ├─ "Read section of a large doc" → doc_toc → doc_read
  │
  ├─ "Find keyword in one doc" → doc_grep → doc_read
  │
  ├─ "Conceptual search in one doc" → doc_query → doc_read
  │
  ├─ "Extract tables/figures" → doc_elements
  │
  ├─ "What links to this page?" → doc_links
  │
  ├─ "Build wiki from source" → wiki_ingest → doc_read → doc_write
  │
  └─ "Check wiki health" → wiki_lint

Troubleshooting

CLI Reference (when MCP is not available)

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qmd status                                # Index health
qmd query "question"                      # Hybrid search (recommended)
qmd search "keywords"                     # BM25 only (fast, no LLM)
qmd get "#abc123"                         # Get by docid
qmd get "docs/readme.md:100" -l 50        # Line slice
qmd multi-get "journals/2026-*.md" -l 40  # Glob batch
qmd multi-get "a.md, b.md, c.md"          # Comma-separated
qmd doc-toc "paper.pdf"                   # Document TOC
qmd doc-read "paper.pdf" "line:45-120"    # Read section
qmd doc-grep "report.md" "revenue"        # Search in document
qmd mcp                                   # MCP server (stdio)
qmd mcp --http --daemon                   # MCP server (HTTP, background)

Setup

For first-time users, use Playbook 0 above — it walks through the full setup interactively, including dependency checks and configuration.

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# Install
npm install -g mineru-document-explorer

# Python dependencies for PDF/DOCX/PPTX (required for binary formats)
pip install pymupdf python-docx python-pptx

# Optional: MinerU Cloud for high-quality PDF (scanned docs, complex layouts)
pip install mineru-open-sdk
export MINERU_API_KEY="your-key"  # get from https://mineru.net

# Index documents
qmd collection add ~/notes --name notes
qmd collection add ~/papers --name papers --mask '**/*.{md,pdf,docx,pptx}'

# Verify
qmd status
qmd search "test query"  # instant, no model download

# Optional: enable semantic search (downloads ~2GB models on first run)
qmd embed

MCP Configuration

Claude Code (~/.claude/settings.json):

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{ "mcpServers": { "qmd": { "command": "qmd", "args": ["mcp"] } } }

Cursor (.cursor/mcp.json) — stdio:

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{ "mcpServers": { "qmd": { "command": "qmd", "args": ["mcp"] } } }

Cursor (.cursor/mcp.json) — HTTP (recommended):

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{ "mcpServers": { "qmd": { "url": "http://localhost:8181/mcp" } } }

Start daemon first: qmd mcp --http --daemon

Skill Installation

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qmd skill install              # install to current project
qmd skill install --global     # install globally