mcp-server

selinazarzour/mcp-server

3.2

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The FastMCP SSE Server is a deployable MCP-compatible remote server designed for academic paper search and information extraction, compatible with Claude, GPT, or any MCP client supporting SSE transport.

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.

🌐 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.