Pdf(paper) Figures Extraction Mcp Serever

by vlln

260 downloads

Not rated

GitHub

Description

# Figure Extractor API and 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…

About

Details

Author: vlln
Downloads: 260
Categories: Search

- Extracts figures, tables, captions, and section titles from PDFs
- Supports both local and remote PDF files
- Provides statistics of extracted tables and figures
- Docker support for easy deployment and scalability
- Offers visualization options for PDF parsing
- MCP server exposes extraction as a tool for AI agents

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Pdf(paper) Figures Extraction Mcp Serever

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

JSON

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

From the repository

Deploy using Docker: clone the repository, build the image, and run the container. After deployment, send a POST request to /api/extract with a PDF file or a PDF URL. The MCP tool (extract_figures_from_pdf) can be invoked via an HTTP request to the /mcp endpoint. The CLI can also be used directly via python figure_extractor.py <path_to_pdf>.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "pdf(paper) figures extraction mcp serever": {
            "pdffigures-mcp-server": {
                "command": "docker",
                "args": [
                    "build",
                    "-t",
                    "pdf-extraction",
                    "."
                ]
            }
        }
    }
}

McpServers

{
    "pdffigures-mcp-server": {
        "command": "docker",
        "args": [
            "build",
            "-t",
            "pdf-extraction",
            "."
        ]
    }
}

Figure Extractor API and 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.

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

mcp_tool_use

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

1. 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.

2. 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.

3. 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

1. Clone the repository:

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

2. Build the Docker image:

    docker build -t pdf-extraction .

3. 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.

No reviews yet — be the first

Use Google, GitHub, or an email account so ratings stay tied to real people.

Email sign in

No reviews posted yet.

Description

About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

McpServers

Figure Extractor API and MCP Server

About PDFFigures 2.0

Features

FastMCP Tool

Use Cases

Setup

Docker Deployment

Test the API

Usage

Extract Figures and Tables from a PDF

Using the API

App Structure

Acknowledgements

License

Install

More like this

Brave Search

🔎 GPT Researcher

Github SSE

AWS MCP Servers

Yoyo

whatsapp-mcp