mcp-document-reader
MCP tool for LLM interaction with EPUB and PDF files.
Documentation
mcp-document-reader
A rudimentary MCP server for interacting with PDF and EPUB documents.
I use this with Windsurf IDE by Codeium, which
only supports MCP tools, not resources.
Installation
Requirements
# Clone the repository
git clone https://github.com/jbchouinard/mcp-document-reader.git
cd mcp-document-reader
poetry installConfigure MCP Server
Run with poetry:
{
"mcpServers": {
"documents": {
"command": "poetry",
"args": ["-C", "path/to/mcp-document-reader", "run", "mcp-document-reader"]
}
}
}Alternatively, build and install with pip, then run the script directly:
poetry build
pipx install dist/*.whl
which mcp-document-readerThen use the following config, with the path output by which:
{
"mcpServers": {
"documents": {
"command": "/path/to/mcp-document-reader",
"args": []
}
}
}Development
Setup
# Install dependencies
poetry installTesting
poetry run pytestLinting
poetry run ruff check --fix .
poetry run ruff format .License
Frequently asked questions
What is mcp-document-reader?
mcp-document-reader is MCP tool for LLM interaction with EPUB and PDF files.
How do I install mcp-document-reader?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is mcp-document-reader open source?
Yes — it is hosted on GitHub at https://github.com/jbchouinard/mcp-document-reader and has 7 stars.
Related MCP tools
This MCP server integrates with your Google Drive and Google Sheets, to enable creating and modifying spreadsheets. Python-based implementation.
🙌 OpenHands: Code Less, Make More for the Model Context Protocol. Enhance AI assistants with powerful integrations. Python-based implementation.
Universal memory layer for AI Agents; Announcing OpenMemory MCP - local and secure memory management. Python-based implementation.
基于大模型搭建的聊天机器人,同时支持 微信公众号、企业微信应用、飞书、钉钉 等接入,可选择ChatGPT/Claude/DeepSeek/文心一言/讯飞星火/通义千问/ Gemini/GLM-4/Kimi/LinkAI,能处理文本、语音和图片,访问操作系统和互联网,支持基于自有知识库进行定制企业智能客服。
An LLM agent that conducts deep research (local and web) on any given topic and generates a long report with citations. Built for the Model Context Protocol to
🚀 The fast, Pythonic way to build MCP servers and clients Trusted by 19900+ developers. Trusted by 19900+ developers. Trusted by 19900+ developers.
Run your own MCP server? See who uses it and what to fix.
Measure it with TrackMCP