title: MCP_Research_Server app_file: main.py sdk: gradio sdk_version: 5.31.0

🧠 FastMCP SSE Server – Research Paper Agent

This project is a deployable MCP-compatible remote server built using the FastMCP framework. It exposes tools and resources for:

• Searching academic papers on arXiv

• Extracting information about saved papers

• Generating structured prompts for Claude or other LLM agents

It is designed to work with Claude, GPT, or any MCP client that supports SSE transport.

Related MCP server: research-mcp

🌐 Live Server

✅ MCP server is running here:
Tool URL (SSE): https://mcp-server-vs1x.onrender.com/sse

To test if it’s working, simply visit the link above — you’ll see a plain text confirmation.

🚀 Features

• search_papers(topic): Search and save top arXiv papers by topic

• extract_info(paper_id): Retrieve paper details from stored JSON

• get_topic_papers(topic): Read summaries for all papers in a topic

• get_available_folders(): List all saved topic folders

• Prompt template for Claude to generate full topic reports

🧑‍💻 Project Structure

.
├── main.py        # Main FastMCP server
├── Dockerfile                # For deployment on Render
├── pyproject.toml            # Python project setup (required by uv)
├── uv.lock                   # Dependency lock file (required by uv)
├── papers/                   # Local storage for downloaded paper info

📦 Requirements

• Python 3.11+

• uv: A fast Python package manager

• Render.com (for deployment)

🛠️ Local Setup (Optional)

git clone https://github.com/YOUR_USERNAME/mcp-sse-server.git
cd mcp-sse-server

# Run with uv (you must have uv installed)
uv pip install --system .
uv run main.py

The server will run on localhost:8001/sse.

☁️ Deploy on Render.com (Docker)

1. Push this project to your GitHub

2. Create a new web service on Render

3. Use the following settings:

   • Environment: Docker

   • Port: 8001

   • Start command: (leave blank – handled in Dockerfile)

4. Deploy 🚀

Render will give you a URL like:

https://your-app-name.onrender.com/sse

To run locally in Docker:

docker run -p 8001:8001 <your-image-name> python main.py

🧪 Test with MCP Inspector

Install and run:

npx @modelcontextprotocol/inspector

In the web UI:

• Transport: SSE

• URL: https://mcp-server-vs1x.onrender.com/sse

You’ll now be able to call the tools and test them live using Claude or your own chatbot.

📚 Credits

Built as part of the DeepLearning.AI Claude Agent Systems course.