MCP Cursor

Enhance your development workflow with AI-powered MCP tools and extensions for Cursor IDE.

Product

  • MCP Servers
  • Getting Started
  • Documentation
  • Open Source

Resources

  • MCP Specification
  • Cursor IDE
  • MCP GitHub
  • Contributing

Legal

  • Privacy Policy
  • Terms of Service
  • Cookie Policy
Made withfor the developer community
© 2026 MCP Cursor. All rights reserved.
MCP Logo
MCP Cursor
IntroductionMCPs
IntroductionMCPs
3D MCP Cursor Visualization
  1. Home
  2. Servers
  3. PDF Search MCP
PDF Search MCP Logo

PDF Search MCP

Model Context Protocol Integration

Overview

Built for Zed to enable semantic searching of PDF documents using a Qdrant vector database and OpenAI embeddings.

PDF Search

Built for Zed to enable semantic searching of PDF documents using a Qdrant vector database and OpenAI embeddings.

Installation Instructions


README: https://github.com/freespirit/pdfsearch-zed

PDF Search for Zed

A document search extension for Zed that lets you semantically search through a PDF document and use the results in Zed's AI Assistant.

Prerequisites

This extension currently requires:

  1. An OpenAI API key (to generate embeddings)
  2. uv installed on your system

Note: While the current setup requires an OpenAI API key for generating embeddings, we plan to implement a self-contained alternative in future versions. Community feedback will help prioritize these improvements.

Quick Start

  1. Clone the repository
git clone https://github.com/freespirit/pdfsearch-zed.git
  1. Set up the Python environment for the MCP server:
cd pdfsearch-zed/pdf_rag
uv venv
uv sync
  1. Install Dev Extension in Zed

  2. Build the search db

cd /path/to/pdfsearch-zed/pdf_rag

echo "OPENAI_API_KEY=sk-..." > src/pdf_rag/.env

# This may take a couple of minutes, depending on the documents' size
# You can provide multiple files and directories as arguments.
#  - files would be chunked.
#  - a directory would be considered as if its files contains chunks.
#    E.g. they won't be further split.
uv run src/pdf_rag/rag.py build "file1.pdf" "dir1" "file2.md" ...
  1. Configure Zed
"context_servers": {
    "pdfsearch-context-server": {
        "settings": {
            "extension_path": "/path/to/pdfsearch-zed"
        }
    }
}

Usage

  1. Open Zed's AI Assistant panel
  2. Type /pdfsearch followed by your search query
  3. The extension will search the PDF and add relevant sections to the AI Assistant's context

Future Improvements

  • Self-contained vector store
  • Self-contained embeddings
  • Automated index building on first run
  • Configurable result size
  • Support for multiple PDFs
  • Optional: Additional file formats beyond PDF

Project Structure

  • pdf_rag/: Python-based MCP server implementation
  • src/: Zed extension code
  • extension.toml and Cargo.toml: Zed extension configuration files

Known Limitations

  • Manual index building is required before first use
  • Requires external services (OpenAI)

Featured MCPs

Github MCP - Model Context Protocol for Cursor IDE

Github

This server provides integration with Github's issue tracking system through MCP, allowing LLMs to interact with Github issues.

Sequential Thinking MCP - Model Context Protocol for Cursor IDE

Sequential Thinking

An MCP server implementation that provides a tool for dynamic and reflective problem-solving through a structured thinking process. Break down complex problems into manageable steps, revise and refine thoughts as understanding deepens, and branch into alternative paths of reasoning.

Puppeteer MCP - Model Context Protocol for Cursor IDE

Puppeteer

A Model Context Protocol server that provides browser automation capabilities using Puppeteer. This server enables LLMs to interact with web pages, take screenshots, execute JavaScript, and perform various browser-based operations in a real browser environment.