asset-aware-mcp 0.6.29

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

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 · Docs Site · GitHub Wiki

🎯 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.

---

✨ Features

• 📄 Asset-Aware ETL - PDF → Markdown with a PyMuPDF-first parser and retained Marker code path:
  • PyMuPDF (default) - Fast extraction (~50MB)
  • Marker (use_marker=True) - High-precision structured parsing code path retained, but packaged runtime remains on security hold in v0.6.29 until upstream marker-pdf supports patched Pillow
• 🧩 Unified Segmentation Export - Normalized segmentation.json merges manifest, blocks, reading order, and persisted markdown line spans for downstream tools and extensions.
• 🖼️ Layout Overlay Debugging - Render page overlays from original.pdf to inspect bbox, segment type, and reading order visually.
• 🔤 On-Demand OCR Preprocessing - Optional ocrmypdf preprocessing path for scanned PDFs before ETL.
• 🧭 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 ingest, Marker-required parse, OCR, and conversion jobs with progress tracking.
• 🗺️ 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.
• 🧾 Verified Citation Bundles - citation_bundle, Foam evidence packs, citation health checks, table/figure evidence notes, and claim promotion export citation-ready spans with locator, quote/hash, context, CRAAP scaffold, and verification status.
• 📝 Docx Editing (DFM) - Edit .docx files in Markdown via Docx-Flavored Markdown format. Supports legacy .doc, .odt, and .ods ingest via LibreOffice auto-conversion. 17 tools: ingest, read, save, list, delete, export, strict round-trip validation, DOCX→PDF/DOC/ODT, table edit planning, 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, document artifacts, citation spans, 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/: 62 tools in 7 modules                   │   │
│  │   document (19) │ docx (17) │ section (5)       │   │
│  │   job (4) │ knowledge (3) │ table (7) │ profile (7) │
│  └─────────────────────────────────────────────────┘   │
│  ┌─────────────────────────────────────────────────┐   │
│  │ resources/: 13 resources in 2 modules           │   │
│  └─────────────────────────────────────────────────┘   │
└─────────────────────┬───────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────┐
│                  ETL Pipeline (DDD)                     │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐              │
│  │ PyMuPDF  │  │  Asset   │  │ LightRAG │              │
│  │ Adapter  │→ │  Parser  │→ │  Index   │              │
│  └──────────┘  └──────────┘  └──────────┘              │
└─────────────────────┬───────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────┐
│                   Local Storage                         │
│  ./data/                                                │
│  ├── {doc_id}/        # PDF document artifacts          │
│  ├── 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

📐 Architecture Diagrams

Visual overview for the project. All diagrams use consistent GitHub README style.

Diagram	Description
01 — System Architecture	Full stack: Telegram → Gateway → MCP Adapter → 3 MCP servers → Ollama
02 — Data Layout	62 tools organized in 7 categories with asset-aware data tree
03 — PDF Ingestion Pipeline	7-stage flow from PDF upload to knowledge graph
04 — DOCX Bidirectional Edit	DOCX ingest → TableContext edit → round-trip save workflow
05 — Knowledge Graph Search	Cross-document search with 3 parallel query paths
06 — Installation Steps	7-step installation from clone to verification
07 — PDF ETL Pipeline	PyMuPDF default path + Marker security-hold diagnostics
08 — KG Architecture	lightrag-hku 3-layer KG architecture
09 — Agent Harness Concept	Assistant harness model for stateless agents

💡 All generation prompts are saved in docs/diagrams/ALL-PROMPTS.md for style consistency and regeneration.

🚀 Quick Start

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

# v0.6.29: Marker extra is temporarily empty because marker-pdf pins
# Pillow<11 while the secure runtime requires Pillow>=12.2.0.
# Use the default PyMuPDF backend until upstream marker-pdf supports patched Pillow.

# 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.

Installation scope note:

• The VS Code extension installs once per user (global). MCP launch env defaults DATA_DIR to workspace ./data and UV_CACHE_DIR to DATA_DIR/.uv-cache; Prepare Server Runtime warms a workspace .uv-cache, falling back to extension global storage only when no workspace is open.
• Runtime data stays with your repo: .env and assetAwareMcp.dataDir default to ./data, so ingested assets and the uv cache used by the launched server remain scoped to the current workspace.

Marker note: Since v0.6.28 the packaged Marker extra has intentionally stayed on security hold: upstream marker-pdf 1.10.2 requires Pillow<11, while this release pins Pillow>=12.2.0 for patched image-processing security. Default installs use the PyMuPDF backend only. use_marker=True / parse_pdf_structure will report that Marker is unavailable until upstream Marker supports a patched Pillow range.

🔌 MCP Tools

Document & Asset Tools

Tool	Purpose
ingest_documents	Process PDF files with PyMuPDF; use_marker=True currently falls back or fails closed while Marker is on security hold
list_documents	List all ingested documents and their asset counts
delete_document	Delete an ingested PDF, its local artifacts, and LightRAG index entries when enabled
convert_pdf_to_docx	Reconstruct a readable DOCX from extracted PDF content; defaults to a conversion background job
convert_pdf_to_pptx	Rebuild editable PPTX slides from extracted PDF markdown and figures; defaults to a conversion background job
inspect_document_manifest	Inspect document structure before fetching specific assets
fetch_document_asset	Precisely retrieve tables (MD) / figures (B64) / sections
parse_pdf_structure	Queue structured parsing work; Marker output remains unavailable until upstream Marker supports patched Pillow
search_source_location	Search exact source locations with page + bbox for verification
export_document_segmentation	Export normalized segmentation.json with reading order + line ranges
visualize_document_layout	Render page overlay images for bbox / type / reading-order inspection
ocr_pdf_document	Run OCR preprocessing and generate a cleaned PDF for later ETL
find_evidence_spans	Search citation-ready spans with source revision, locator, hash, and CRAAP scaffold
verify_citation_ref	Verify span AssetRefs against the current citation index and locator metadata
citation_bundle	Export verified evidence bundles with AssetRef, quote/hash, locator, context, CRAAP scaffold, and verification status
document	Operation-based facade over PDF ingest/list/delete/inspect/parse
document_asset	Operation-based facade over asset fetch and section tree/detail/blocks/search
evidence	Operation-based facade over citation span find/verify/source-location search and bundle export
convert_document	Operation-based facade for PDF, DOCX/DFM, and Markdown conversions; conversion paths default to background jobs

Job Management Tools

Tool	Purpose
get_job_status	Get async ingestion/conversion job progress and final result
list_jobs	List active or historical ETL jobs
cancel_job	Cancel a running ETL job
job	Operation-based facade over job get/list/cancel

Knowledge Graph Tools

Tool	Purpose
consult_knowledge_graph	Citation-aware knowledge graph query with structured, data, text, and optional verified evidence bundles
export_knowledge_graph	Export graph summary / JSON / Mermaid for inspection
knowledge	Operation-based facade over knowledge graph consult/export

Knowledge graph note:

• consult_knowledge_graph defaults to response_mode="structured" and can return answer, references, metadata, retrieval, counts, and verified_evidence when verify_references=true.
• Use response_mode="data" when you want retrieval payloads without final answer synthesis, or response_mode="text" for legacy plain-text behavior.

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; defaults to a conversion background job
convert_docx_to_doc	Export the current DOCX/DFM state to DOC in fidelity mode; defaults to a conversion background job
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
docx_table_edit_plan	Preview table cell/row/column/header changes and structural risks before write-back
export_markdown	Export Markdown to .docx/.pdf/.doc; defaults to a conversion background job
convert_docx_to_odt	Export the current DOCX/DFM state to ODT; defaults to a conversion background job
docx	Operation-based facade over DOCX/DFM ingest/get/save/list/delete/blocks/validate
docx_table	Operation-based facade over DOCX table to_context/from_context/chart_data/edit_plan

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
detect_etl_profile	Detect the best built-in profile from PDF path, doc_id, or sample text
etl_profile	Operation-based facade over profile list/get/current/set/load/detect

🔧 Tech Stack

Category	Technology
Language	Python 3.10+
Package Manager	uv (all pip/setup-python removed)
ETL	PyMuPDF (fitz); Marker is temporarily on security hold
RAG	LightRAG (lightrag-hku)
MCP	FastMCP
Storage	Local filesystem (JSON/Markdown/PNG)

📋 Documentation

Installation guidance:

• Default install: uv sync

• Marker backend: temporarily disabled in v0.6.29 because marker-pdf pins vulnerable Pillow<11; the marker / pdf extras are compatibility placeholders until upstream supports patched Pillow.

• VS Code extension: assetAwareMcp.enableMarkerBackend is retained as a setting, but the launcher will not install marker-pdf while the security hold is active.

• Technical Spec - Detailed technical specification

• Architecture - System architecture

• Constitution - Project principles

• Competitive Analysis - MCP + DOCX ecosystem landscape

📄 License

Apache License 2.0