🧠 FastMCP SSE Server – Research Paper Agent

@selinazarzour

About 🧠 FastMCP SSE Server – Research Paper Agent

No overview available yet

#mcp-server #research-paper-agent #fastmcp

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-server-selinazarzour": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "--system",
        "."
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is 🧠 FastMCP SSE Server – Research Paper Agent?

A deployable MCP-compatible remote server built with FastMCP. It exposes tools for searching academic papers on arXiv, extracting saved paper details, and generating structured prompts for Claude or other LLM agents.

How to use 🧠 FastMCP SSE Server – Research Paper Agent?

Connect any MCP client that supports SSE transport (e.g., Claude, GPT) to the live server at https://mcp-server-vs1x.onrender.com/sse. For local use, clone the repo, install dependencies with uv, and run uv run main.py to start the server on localhost:8001/sse.

Key features of 🧠 FastMCP SSE Server – Research Paper Agent

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
Uses SSE transport for remote MCP client communication

Use cases of 🧠 FastMCP SSE Server – Research Paper Agent

Automate literature searches on arXiv for any research topic
Build agent workflows that extract and store metadata from academic papers
Generate structured research reports with Claude using the built‑in prompt template

FAQ from 🧠 FastMCP SSE Server – Research Paper Agent

What does this server do?

It is an MCP remote server that provides tools to search arXiv papers, extract saved paper info, list topics, and generate structured prompts for LLMs.

What transport does it use?

It uses SSE (Server‑Sent Events) transport, making it compatible with any MCP client that supports SSE.

What are the runtime requirements?

Python 3.11+, the uv package manager, and optionally Docker for deployment on Render.com.

How can I test the server locally or remotely?

Remotely, visit the live SSE URL or use the MCP Inspector (npx @modelcontextprotocol/inspector) with SSE transport and the server’s URL. Locally, run uv run main.py and connect to localhost:8001/sse.

How is data stored?

Paper information is saved locally in a papers/ directory as JSON files when using search and extraction tools.

Frequently asked questions