A Model Context Protocol (MCP) server that enables Claude Desktop to analyze PDF documents using Google's AI models. This server sends PDF files & extracts text from PDFs by leveraging Gemini's…
CLDGeminiPDF MCP Server is an MCP project maintained in the tfll37/cldgeminipdf-analyzer repository. The maintainer describes its purpose as “A Model Context Protocol (MCP) server that enables Claude Desktop to analyze PDF documents using Google's AI models. This server sends PDF files & extracts…”. Read the project documentation for the complete scope.
The project provides a local client configuration using java. Follow the repository’s build and installation steps before copying it, and replace example paths with the paths on your own computer.
The example declares GEMINI_API_KEY, GEMINI_MODEL. Secret-looking values have been replaced with placeholders. Required and optional labels are shown only when the documentation states them.
Project documentation · Repository · Discovery listing. This profile records the public documentation checked on 2026-10-08.
{
"mcpServers": {
"CLDGeminiPDF": {
"command": "java",
"args": [
"-jar",
"/path/to/CLDGeminiPDF.v1.0.0.jar"
],
"env": {
"GEMINI_API_KEY": "YOUR_GEMINI_API_KEY",
"GEMINI_MODEL": "gemini-2.0-flash"
}
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
| Variable | Description | Required |
|---|---|---|
| GEMINI_API_KEY | Declared in the maintainer’s client configuration. | Check documentation |
| GEMINI_MODEL | Declared in the maintainer’s client configuration. | Check documentation |
The original Chromium automation reference server — simple, screenshot-driven browser control.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Automates browsers and APIs, enabling Large Language Models (LLMs) to interact with web pages for tasks like screenshot capture and JavaScript
Workers, KV, R2 and D1 by conversation — Cloudflare's official remote servers for building and observability.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.