⚡ PDF Reader MCP Server

High-performance, ultra-low-token Model Context Protocol (MCP) server for LLMs and AI coding assistants.
Powered by C-accelerated PyMuPDF and pymupdf4llm for pristine Markdown and table extraction.

🎯 Why This MCP Server?

When AI agents (Claude, Gemini, Cursor, Antigravity) process PDFs through native chat uploads, the interface typically converts every page into high-resolution images (Vision) and raw OCR text:

• ⚠️ Massive Token Waste: A 13-page technical document consumes 30,000–50,000 tokens. A 370-page service manual easily exceeds 1,000,000+ tokens, blowing past context limits.

• ⚠️ High Latency: Uploading and parsing takes 15–40 seconds per interaction.

• ⚠️ Loss of Formatting: Tables and multi-column layouts frequently lose their structure in raw text extraction.

The Solution: On-Demand Introspection

pdf-reader-mcp allows AI agents to inspect, search, and extract only the specific sections or pages they actually need:

• 🚀 2.1 seconds to search an entire 373-page automotive technical manual.

• 📉 98–99% token reduction: ~200 tokens per focused query instead of 50,000+.

• 📊 Table-Aware Markdown: Preserves tables, headers, lists, and code blocks using pymupdf4llm.

Related MCP server: PDF Splitter MCP Server

🛠️ Available MCP Tools

📦 Installation

Prerequisites

• Python 3.10+ (tested on Python 3.11 and 3.12)

• Windows, macOS, or Linux

git clone https://github.com/serresident/pdf-reader-mcp.git
cd pdf-reader-mcp
pip install -r requirements.txt

⚙️ Configuration

1. Antigravity / Gemini CLI (mcp_config.json)

Add to ~/.gemini/config/mcp_config.json:

{
  "mcpServers": {
    "pdf-reader-mcp": {
      "command": "python",
      "args": [
        "C:\\Users\\erch\\Мой диск\\1 for ai\\mcp\\pdf-reader-mcp\\pdf_reader_mcp.py"
      ]
    }
  }
}

2. Claude Desktop (claude_desktop_config.json)

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

{
  "mcpServers": {
    "pdf-reader": {
      "command": "python",
      "args": [
        "/path/to/pdf-reader-mcp/pdf_reader_mcp.py"
      ]
    }
  }
}

3. Cursor / Windsurf

Add as a Command (stdio) MCP server in the Settings → MCP panel:

• Command: python

• Args: /absolute/path/to/pdf_reader_mcp.py

🔍 How Agents Use It (Workflow)

flowchart TD
    User["User asks question about PDF"] --> Agent["AI Agent"]
    Agent -->|1. Check pages & size| Info["pdf_info"]
    Agent -->|2. Check document structure| TOC["pdf_toc"]
    Agent -->|3. Find keywords across 400 pages| Search["pdf_search"]
    Search -->|Returns matching pages e.g. p. 36| Match["Target Pages Identified"]
    Match -->|4. Read only relevant page| Read["pdf_read_page(36)"]
    Read -->|Markdown + Tables| Agent
    Agent --> Answer["Fast, accurate answer with zero context bloat!"]

🧪 Testing

Run an end-to-end self-test directly in terminal:

python -c "
import subprocess, json
proc = subprocess.Popen(['python', 'pdf_reader_mcp.py'], stdin=subprocess.PIPE, stdout=subprocess.PIPE, text=True, encoding='utf-8')
proc.stdin.write(json.dumps({'jsonrpc': '2.0', 'id': 1, 'method': 'initialize', 'params': {}}) + '\n')
proc.stdin.flush()
print('Server initialized:', proc.stdout.readline())
proc.terminate()
"

📄 License

MIT License © 2026 serresident.