README.md

metadata

title: MCP_Research_Server
app_file: research_server.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.

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🌐 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.

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🚀 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

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🧑‍💻 Project Structure

.
├── research_server.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

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📦 Requirements

• Python 3.11+
• uv: A fast Python package manager
• Render.com (for deployment)

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🛠️ 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 research_server.py

The server will run on localhost:8001/sse.

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☁️ 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 research_server.py

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🧪 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.

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📚 Credits

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