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Pdffigures MCP Server

Extract figures and tables from PDF documents using this FastAPI-based service. The Figure Extractor API and MCP Server provides a straightforward HTTP interface for PDFFigures 2.0, a robust figure extraction system developed by the Allen Institute for AI.

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Figure Extractor API and MCP Server

This API wrapper makes it ideal for integration into various applications and workflows, particularly for Retrieval-Augmented Generation (RAG) applications.

The MCP Server, powered by FastMCP, exposes the PDF extraction functionality as a MCP tool. This allows for seamless integration with AI Agents and workflows that can automatically call the extraction service.

The server hosts the extract_figures_from_pdf tool, which can be invoked via an HTTP request to the /mcp endpoint. This tool takes a PDF URL, processes the document, and returns the extracted figures and tables in a structured JSON format.

The default MCP Server url: http://localhost:5001/mcp

About PDFFigures 2.0

This API service is built on top of PDFFigures 2.0, a Scala-based project by the Allen Institute for AI. PDFFigures 2.0 is specifically designed to extract figures, captions, tables, and section titles from scholarly documents in computer science domain. The original work is described in their academic paper: "PDFFigures 2.0: Mining Figures from Research Papers" (Clark and Divvala, 2016). You can read the paper here and visit the PDFFigures 2.0 website.

┌─────────────────┐      ┌──────────────────┐      ┌────────────────┐
│   Your App      │ HTTP │ Figure Extractor │ JNI  │  PDFFigures    │
│  (Any Language) │──────►  API & MCP Server│──────►     2.0        │
│                 │      │  Python(FastAPI) │      │  (Scala/JVM)   │
└─────────────────┘      └──────────────────┘      └────────────────┘

Features

PDF figure and table extraction
Support for local and remote PDF files
Statistics of the extracted tables and figures
Docker support for easy deployment
Visualization options for PDF parsing

FastMCP Tool

This project now includes a FastMCP tool that allows calling the PDF extraction service programmatically. The extract_figures_from_pdf tool can be used to extract figures from a PDF given a URL.

Use Cases

Machine Learning Dataset Creation Extract visual data from clinical trial reports and research papers to build training datasets for medical image analysis and AI models, enabling researchers to efficiently aggregate figures for training machine learning algorithms in healthcare diagnostics.
Clinical Research Data Mining Automatically extract and catalog figures from medical research articles, capturing key visualizations like treatment effect graphs, patient outcome charts, and experimental result diagrams to support systematic reviews and meta-analysis.
Academic Literature Review and Education Quickly compile comprehensive visual libraries from academic publications, allowing researchers and educators to create teaching resources, compare research methodologies, and track visual trends across scientific disciplines.

Setup

Docker Deployment

Clone the repository:

git clone https://github.com/Huang-lab/figure-extractor.git
cd pdf-extraction

Build the Docker image:
```
docker build -t pdf-extraction .
```
Run the Docker container:
```
docker run -p 5001:5001 pdf-extraction
```

Test the API

Open a web browser and navigate to http://localhost:5001/docs to view the API documentation.
Use the CLI to test the API. For example, to extract figures from a PDF, run the following command:
```
python figure_extractor.py <path_to_your_pdf>
```

Usage

Extract Figures and Tables from a PDF

The /api/extract endpoint now supports both PDF file uploads and PDF URLs.

Using the API

You can send a POST request to /api/extract with either:

A file (multipart/form-data) containing the PDF.
A pdf_url (form-data) containing the URL of the PDF.

The API will return a JSON response with extracted figures and tables, including full renderURL paths.

A JSON response example (imageText truncated for brevity):

[
  {
    "caption": "TABLE III CMAPSS DATASET ATTRIBUTES",
    "captionBoundary": {
      "x1": 113.12599182128906,
      "x2": 225.1184844970703,
      "y1": 116.80506896972656,
      "y2": 130.57305908203125
    },
    "figType": "Table",
    "imageText": [
      "Required", "fan", "conversion", "speed", "rpm", "High-pressure", "turbines", "cool", "air", "flow",
      "lbm/s", "Low-pressure", "turbines", "cool", "air", "flow", "lbm/s", "Bleed", "enthalpy", "-", "Required"
    ],
    "name": "III",
    "page": 5,
    "regionBoundary": {
      "x1": 46.8,
      "x2": 291.12,
      "y1": 140.88,
      "y2": 385.91999999999996
    },
    "renderDpi": 300,
    "renderURL": "http://localhost:5001/resources/4-TableIII-1.png"
  },
  {
    "caption": "Fig. 3. Agentic AI implementation with Google ADK.",
    "captionBoundary": {
      "x1": 335.4129943847656,
      "x2": 516.9002685546875,
      "y1": 228.6050567626953,
      "y2": 233.40704345703125
    },
    "figType": "Figure",
    "imageText": [],
    "name": "3",
    "page": 5,
    "regionBoundary": {
      "x1": 302.88,
      "x2": 549.12,
      "y1": 86.88,
      "y2": 216
    },
    "renderDpi": 300,
    "renderURL": "http://localhost:5001/resources/4-Figure3-1.png"
  },
  {
    "caption": "TABLE I COMPARISON BETWEEN AI AGENTS AND AGENTIC AI",
    "captionBoundary": {
      "x1": 204.9189910888672,
      "x2": 390.3569641113281,
      "y1": 54.10902404785156,
      "y2": 67.87701416015625
    },
    "figType": "Table",
    "imageText": [
      "pert", "systems", "LLM-based", "agents,", "multi-agent", "coordination,", "intent-based", "workflows", "Task",
      "Scope", "Focused", "on", "short-term,", "well-defined", "tasks", "Oriented", "toward", "long-term,", "dynamic,",
    ],
    "name": "I",
    "page": 2,
    "regionBoundary": {
      "x1": 51.839999999999996,
      "x2": 543.12,
      "y1": 77.75999999999999,
      "y2": 217.92
    },
    "renderDpi": 300,
    "renderURL": "http://localhost:5001/resources/4-TableI-1.png"
  },
  {
    "caption": "Fig. 1. Traditional AI Agent vs. Agentic AI",
    "captionBoundary": {
      "x1": 224.30499267578125,
      "x2": 370.9708251953125,
      "y1": 448.1660461425781,
      "y2": 452.968017578125
    },
    "figType": "Figure",
    "imageText": [],
    "name": "1",
    "page": 2,
    "regionBoundary": {
      "x1": 45.839999999999996,
      "x2": 549.12,
      "y1": 230.88,
      "y2": 436.08
    },
    "renderDpi": 300,
    "renderURL": "http://localhost:5001/resources/4-Figure1-1.png"
  },
  {
    "caption": "TABLE IV SUMMARY OF ENGINE MAINTENANCE ACTIONS",
    "captionBoundary": {
      "x1": 215.99301147460938,
      "x2": 379.2913513183594,
      "y1": 54.10902404785156,
      "y2": 67.87701416015625
    },
    "figType": "Table",
    "imageText": [
      "#", "Engines", "RUL", "Range", "Recommended", "Action", "Priority", "Cost", "(USD)", "Labor", "Hours", "Assigned",
      "Staff", "Scheduled", "Time", "15", "82–124", "MONITOR", "low", "0", "0", "[jr", "mechanic]", "Within", "7", "days"
    ],
    "name": "IV",
    "page": 7,
    "regionBoundary": {
      "x1": 48.96,
      "x2": 549.12,
      "y1": 77.75999999999999,
      "y2": 135.12
    },
    "renderDpi": 300,
    "renderURL": "http://localhost:5001/resources/4-TableIV-1.png"
  },
  {
    "caption": "TABLE II KEY COMPONENTS OF INTENTION PROCESSING",
    "captionBoundary": {
      "x1": 345.09295654296875,
      "x2": 507.22100830078125,
      "y1": 417.8482666015625,
      "y2": 431.6162414550781
    },
    "figType": "Table",
    "imageText": [
      "Targets", "Specify", "the", "resources", "or", "entities", "to", "which", "the", "intent", "applies.", "Can", "be",
      "defined", "statically", "(explicit", "list)", "or", "dynamically", "(using", "filters", "or", "criteria).", "Context"
    ],
    "name": "II",
    "page": 3,
    "regionBoundary": {
      "x1": 302.88,
      "x2": 549.12,
      "y1": 441.84,
      "y2": 677.04
    },
    "renderDpi": 300,
    "renderURL": "http://localhost:5001/resources/4-TableII-1.png"
  },
  {
    "caption": "Fig. 2. Proposed framework for Industry 5.0 applying intent-based and Agentic AI.",
    "captionBoundary": {
      "x1": 159.51600646972656,
      "x2": 435.7596435546875,
      "y1": 288.3310241699219,
      "y2": 293.13299560546875
    },
    "figType": "Figure",
    "imageText": [],
    "name": "2",
    "page": 4,
    "regionBoundary": {
      "x1": 45.839999999999996,
      "x2": 549.12,
      "y1": 49.68,
      "y2": 276
    },
    "renderDpi": 300,
    "renderURL": "http://localhost:5001/resources/4-Figure2-1.png"
  }
]

App Structure

project/
├── Dockerfile                # Defines the Docker image for the FastAPI web service
├── Dockerignore    
├── app/                      # Contains the FastAPI web service code
│   ├── __init__.py           # Initializes the FastAPI app
│   ├── app.py                # Defines the API endpoints and MCP Server
│   ├── service.py            # Contains the logic for running `pdffigures2`
│   └── utils.py              # Utility functions for file handling
├── figure_extractor.py       # CLI & Module for extracting figures and tables from a PDF file
└── README.md

Acknowledgements

This project is a fork of Huang-lab/figure-extractor. We are grateful to the original authors for their work.

License

This project is licensed under the Apache License 2.0.