asset-aware-mcp

🏥 Medical RAG with Asset-Aware MCP - Precise PDF asset retrieval (tables, figures, sections) and Knowledge Graph for AI Agents.

🌐 繁體中文

🎯 Why Asset-Aware MCP?

AI cannot directly read image files on your computer. This is a common misconception.

Method	Can AI analyze image content?	Description
❌ Provide PNG path	No	AI cannot access the local file system
✅ Asset-Aware MCP	Yes	Retrieves Base64 via MCP, allowing AI vision to understand directly

Real-world Effect

# After retrieving the image via MCP, the AI can analyze it directly:

User: What is this figure about?

AI: This is the architecture diagram for Scaled Dot-Product Attention:
    1. Inputs: Q (Query), K (Key), V (Value)
    2. MatMul of Q and K
    3. Scale (1/√dₖ)
    4. Optional Mask (for decoder)
    5. SoftMax normalization
    6. Final MatMul with V to get the output

This is the value of Asset-Aware MCP - enabling AI Agents to truly "see" and understand charts and tables in your PDF literature.

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✨ Features

• 📄 Asset-Aware ETL - PDF → Markdown with dual-engine PDF parsing:
  • PyMuPDF (default) - Fast extraction (~50MB)
  • Marker (optional, use_marker=True) - High-precision structured parsing with blocks.json (bbox/coordinates)
• 🧭 Section Navigation - Dynamic hierarchy section tree with 5 tools: browse, search, detail, content reading, and block extraction for any depth of headings.
• 🔄 Async Job Pipeline - Supports asynchronous task processing and progress tracking for large documents.
• 🗺️ Document Manifest - Provides a structured "map" of the document for precise data access by Agents.
• 🧠 LightRAG Integration - Knowledge Graph + Vector Index, supporting cross-document comparison and reasoning.
• 📝 Docx Editing (DFM) - Edit .docx files in Markdown via Docx-Flavored Markdown format. Supports legacy .doc files (auto-converts via LibreOffice). 13 tools: ingest, read, save, list, delete, export, strict round-trip validation, DOCX→PDF, DOCX→DOC, and Docx ↔ A2T bridges.
• 🛡️ DFM Integrity Checker - Automatic validation and auto-repair at every pipeline stage (post-ingest, pre-save, post-save). Catches orphan markers, column mismatches, and format inconsistencies.
• 📊 A2T (Anything to Table) - 7 operation-based tools for building professional tables from any source (PDF assets, Knowledge Graph, URLs, user input). Features: Citations (AssetRef), Audit Trail, Schema Evolution, Templates, Drafting, and Token-efficient resumption.
• 🖥️ VS Code Management Extension - Graphical interface for monitoring server status, ingested documents, and A2T tables/drafts with one-click Excel export.
• 🔌 MCP Server - Exposes tools and resources to Copilot/Claude via FastMCP.
• 🏥 Medical Research Focus - Optimized for medical literature, supporting Base64 image transmission for Vision AI analysis.

🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                    AI Agent (Copilot)                   │
└─────────────────────┬───────────────────────────────────┘
                      │ MCP Protocol (Tools & Resources)
┌─────────────────────▼───────────────────────────────────┐
│            MCP Server (Modular Presentation)            │
│  ┌─────────────────────────────────────────────────┐   │
│  │ tools/: 43 tools in 7 modules                   │   │
│  │   document (8) │ docx (13) │ section (5)        │   │
│  │   job (3) │ knowledge (2) │ table (7) │ profile (5) │
│  └─────────────────────────────────────────────────┘   │
│  ┌─────────────────────────────────────────────────┐   │
│  │ resources/: 12 resources in 2 modules           │   │
│  └─────────────────────────────────────────────────┘   │
└─────────────────────┬───────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────┐
│                  ETL Pipeline (DDD)                     │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐              │
│  │ PyMuPDF  │  │  Asset   │  │ LightRAG │              │
│  │ Adapter  │→ │  Parser  │→ │  Index   │              │
│  └──────────┘  └──────────┘  └──────────┘              │
└─────────────────────┬───────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────┐
│                   Local Storage                         │
│  ./data/                                                │
│  ├── doc_{id}/        # Document Assets                 │
│  ├── docx_{id}/       # Docx IR + DFM + Assets          │
│  ├── tables/          # A2T Tables (JSON/MD/XLSX)       │
│  │   └── drafts/      # Table Drafts (Persistence)      │
│  └── lightrag_db/     # Knowledge Graph                 │
└─────────────────────────────────────────────────────────┘

📁 Project Structure (DDD)

asset-aware-mcp/
├── src/
│   ├── domain/              # 🔵 Domain: Entities, Value Objects, Interfaces
│   ├── application/         # 🟢 Application: Doc Service, Table Service (A2T), Asset Service
│   ├── infrastructure/      # 🟠 Infrastructure: PyMuPDF, LightRAG, Excel Renderer
│   └── presentation/        # 🔴 Presentation: MCP Server (FastMCP)
├── data/                    # Document and Asset Storage
├── docs/
│   └── spec.md              # Technical Specification
├── tests/                   # Unit and Integration Tests
├── vscode-extension/        # VS Code Management Extension
└── pyproject.toml           # uv Project Config

🚀 Quick Start

# Install dependencies (using uv) — default install skips Marker/torch
uv sync

# Optional: install Marker backend only if you need structured parsing
uv sync --extra marker

# Run MCP Server
uv run python -m src.presentation.server

# Or use the VS Code extension for graphical management

Runtime note: The VS Code extension prefers a managed Python 3.11 runtime when launching the MCP server via uv or uvx. This avoids native package builds on end-user machines, especially macOS systems without Xcode Command Line Tools, while keeping the project itself compatible with newer Python versions.

Marker note: marker-pdf is now an optional dependency because it may pull in torch, surya, and platform-specific ML wheels. Default installs use the PyMuPDF backend only. Enable Marker only when you need use_marker=True or parse_pdf_structure.

🔌 MCP Tools

Document & Asset Tools

Tool	Purpose
ingest_documents	Process PDF files with optional Marker backend (use_marker=True for blocks.json)
list_documents	List all ingested documents and their asset counts
delete_document	Delete an ingested PDF and its local artifacts
convert_pdf_to_docx	Reconstruct a readable DOCX from extracted PDF content
inspect_document_manifest	Inspect document structure before fetching specific assets
fetch_document_asset	Precisely retrieve tables (MD) / figures (B64) / sections
parse_pdf_structure	Run high-precision Marker parsing and emit structured blocks
search_source_location	Search exact source locations with page + bbox for verification

Job Management Tools

Tool	Purpose
get_job_status	Get async ingestion job progress and final result
list_jobs	List active or historical ETL jobs
cancel_job	Cancel a running ETL job

Knowledge Graph Tools

Tool	Purpose
consult_knowledge_graph	Knowledge graph query, cross-document comparison
export_knowledge_graph	Export graph summary / JSON / Mermaid for inspection

Section Navigation Tools (Dynamic Hierarchy)

Tool	Purpose
list_section_tree	Display complete section hierarchy tree (supports any depth)
get_section_detail	Get detailed info for a specific section
get_section_blocks	Extract all blocks from a section with page + bbox
search_sections	Search section titles
get_section_content	Read section content via asset service

Docx Editing Tools (DFM — Docx-Flavored Markdown)

Edit .docx files as Markdown. Preserves formatting, tables, media on round-trip.

Tool	Purpose
ingest_docx	Import .docx and decompose into DFM blocks
get_docx_content	Read DFM content of specific blocks
save_docx	Write DFM edits back to .docx
list_docx_blocks	List document block structure
list_docx_documents	List all ingested DOCX/DFM documents
delete_docx	Delete an ingested DOCX/DFM document and its local artifacts
convert_docx_to_pdf	Export the current DOCX/DFM state to PDF in fidelity mode
convert_docx_to_doc	Export the current DOCX/DFM state to DOC in fidelity mode
docx_validate_roundtrip	6-dimension round-trip fidelity validation + file-level comparison (SHA-256, ZIP diff)
docx_table_to_context	Bridge: Docx table → A2T context
docx_table_from_context	Bridge: A2T table → Docx table
docx_chart_data	Extract chart data from Docx
export_markdown	Export Markdown to .docx/.pdf/.doc

A2T (Anything to Table) Tools — 7 Operation-Based Tools

Agent-friendly design: each tool handles multiple operations via operation parameter. Tables accept any source — PDF assets, KG entities, external URLs, or user input.

Tool	Operations	Purpose
plan_table	schema / templates / from_template	Schema planning, browse 4 built-in templates, create from template
table_manage	create / delete / list / preview / resume / render / add_column / remove_column / rename_column	Table lifecycle + Schema evolution
table_data	add_rows / get_row / update_row / delete_row / get_cell / update_cell / clear_cell	Row & cell CRUD
table_cite	add / get / remove / cell_history	Citation management with AssetRef (7 source types)
table_history	changes / tokens	Audit trail & token estimation
table_draft	create / update / add_rows / resume / commit / list / delete	Draft workflow with persistence
discover_sources	—	Cross-document source discovery (sections, tables, figures, KG)

ETL Profile Tools

Different journals/formats need different extraction settings. Use these tools to switch profiles.

Tool	Purpose
list_etl_profiles	List all available profiles (default, arxiv, nature, ieee, elsevier)
get_etl_profile	Get detailed configuration of a specific profile
get_current_etl_profile	Show currently active profile
set_etl_profile	Switch profile for subsequent document ingestion
load_etl_profile_from_json	Load custom profile from JSON file

🔧 Tech Stack

Category	Technology
Language	Python 3.10+
Package Manager	uv (all pip/setup-python removed)
ETL	PyMuPDF (fitz) + Marker (optional, high-precision)
RAG	LightRAG (lightrag-hku)
MCP	FastMCP
Storage	Local filesystem (JSON/Markdown/PNG)

📋 Documentation

Installation guidance:

• Default install: uv sync

• Install Marker backend only when needed: uv sync --extra marker

• Safer extension Marker setup: enable Marker backend in settings and keep torchBackend=cpu unless you explicitly need GPU wheels

• Technical Spec - Detailed technical specification

• Architecture - System architecture

• Constitution - Project principles

• Competitive Analysis - MCP + DOCX ecosystem landscape

📄 License

Apache License 2.0