pdf-reader MCP server

An MCP server for reading PDFs

Components

Resources

The server provides academic-aware PDF resources with:

Custom file:// URI scheme for accessing individual PDFs
Academic structure detection and key section extraction
Metadata enriched with document type classification
Resources optimized for agent understanding

Academic Prompts

The server provides specialized academic analysis prompts:

summarize-academic-paper: Intelligent academic paper summarization
- Required "file_path" argument for PDF location
- Optional "focus" argument (general/methodology/results/implications)
- Generates prompts with key sections, citations, and metadata
analyze-research-methodology: Deep methodology analysis
- Required "file_path" argument for PDF location
- Focuses on research design, data collection, and statistical methods

Enhanced Tools

Basic PDF Processing:

load-pdf: Load and cache a PDF file for processing
get-metadata: Get PDF metadata and document information
extract-images: Extract embedded images with metadata
render-page: Render PDF pages as high-resolution images

Academic Enhancements:

extract-academic-text: Text extraction with proper reading order and math formula preservation
detect-sections: Identify academic sections (Abstract, Introduction, Methods, Results, etc.)
extract-abstract: Specifically extract the abstract section
extract-key-sections: Get key sections optimized for agent understanding
extract-citations: Parse in-text citations and reference lists
chunk-content: Break content into agent-friendly semantic chunks
analyze-document-structure: Comprehensive academic document analysis

Configuration

This PDF reader MCP server provides comprehensive PDF processing capabilities including:

Full text extraction from any PDF
High-resolution image extraction
Table detection and extraction
Annotation and comment extraction
PDF metadata retrieval
Page rendering to images
Document structure analysis

Installation & Setup

Prerequisites

Python 3.13 or higher
uv package manager (install with pip install uv)

Install Dependencies

uv sync

IDE Integration

VSCode with MCP Extension

Install the MCP VSCode Extension
Open your VSCode settings (.vscode/settings.json) and add:

{ "mcp.servers": { "pdf-reader": { "url": "http://localhost:8000/sse", "description": "PDF reader with full extraction capabilities" } } }

WindSurf IDE

Open WindSurf settings
Navigate to Extensions → MCP Servers
Add a new server configuration:

{ "name": "pdf-reader", "url": "http://localhost:8000/sse", "description": "Comprehensive PDF processing server" }

Cursor IDE

Open Cursor settings (Cmd/Ctrl + ,)
Search for "MCP" or navigate to Extensions → MCP
Add server configuration:

{ "mcpServers": { "pdf-reader": { "url": "http://localhost:8000/sse", "description": "PDF reader with text, image, and table extraction" } } }

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

{ "mcpServers": { "pdf-reader": { "url": "http://localhost:8000/sse", "description": "PDF reader with comprehensive extraction capabilities" } } }

Starting the Server

Before using the PDF reader in any IDE, start the HTTP server:

# Navigate to the pdf-reader directory cd /path/to/your/pdf-reader # Start the server uv run pdf-reader

The server will start on http://localhost:8000 with the MCP SSE endpoint available at /sse for all IDEs to connect to.

Usage Examples

Once configured in your IDE, you can use the PDF reader with natural language commands:

Basic PDF Processing

"Load the research paper at /path/to/paper.pdf and extract all the text" "Get metadata for the PDF document at /documents/report.pdf" "Extract all images from the PDF on page 3"

Advanced Analysis

"Summarize the PDF document in technical style focusing on methodology" "Analyze the structure of this PDF and tell me about its organization" "Extract all tables from the document and show me the data"

Visual Processing

"Render page 5 of the PDF as a high-resolution image" "Extract all annotations and comments from this PDF" "Show me all the images embedded in this document"

Available Tools

Tool	Description	Parameters
`load-pdf`	Load and cache PDF	`file_path` , optional `name`
`extract-text`	Extract text content	`file_path` , optional `page`
`extract-images`	Extract embedded images	`file_path` , optional `page`
`get-metadata`	Get document metadata	`file_path`
`extract-tables`	Extract table data	`file_path` , optional `page`
`extract-annotations`	Extract comments/highlights	`file_path`
`render-page`	Render page as image	`file_path` , `page` , optional `dpi`

Development

Building and Publishing

To prepare the package for distribution:

Sync dependencies and update lockfile:

uv sync

Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

Publish to PyPI:

uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

Token: --token or UV_PUBLISH_TOKEN
Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory /Users/cloudchase/Desktop/AverageJoesLab/mcp-servers/pdf-reader run pdf-reader

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

This server cannot be installed

-

security - not tested

F

license - not found

-

quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

An MCP server that provides comprehensive PDF processing capabilities including text extraction, image extraction, table detection, annotation extraction, metadata retrieval, page rendering, and document structure analysis.

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