Research Paper MCP Server

A lightweight Python MCP (Model Context Protocol) server that enables AI assistants to search and retrieve research papers from arXiv.

Built using the official Python MCP SDK and the arXiv API.

Features

The server exposes two MCP tools:

Returned metadata includes paper titles, authors, publication dates, abstracts, and URLs.

Related MCP server: arxiv-search MCP Server

Architecture

AI Assistant / MCP Inspector
           |
           | MCP (stdio)
           v
    Python MCP Server
           |
           | arXiv API
           v
        arXiv
           |
           v
   Structured Paper Metadata

Prerequisites

• Python 3.11+

• uv for Python dependency management

• Node.js and npx for MCP Inspector

• Internet access for retrieving papers

No API key is required.

Installation

1. Set up the project

Create a directory and initialize the project:

uv init research-paper-mcp
cd research-paper-mcp

If you have cloned this repository instead, navigate to the project directory and run uv sync.

2. Install dependencies

uv add "mcp[cli]" arxiv

This installs the MCP Python SDK, development CLI, and arXiv Python client.

3. Project structure

research-paper-mcp/
├── server.py
├── pyproject.toml
├── uv.lock
└── README.md

The MCP server implementation is contained in server.py.

Running the Server

1. Launch MCP Inspector

From the project directory, run:

uv run mcp dev server.py

This starts the MCP Inspector development environment.

Open the local URL displayed in your terminal.

2. Connect to the MCP server

In the MCP Inspector browser interface:

1. Locate the Connection panel.

2. Select STDIO as the transport type.

3. If the connection settings are already populated, leave them unchanged.

4. Otherwise, configure the command to launch the server from the project directory.

5. Click Connect.

Keep the original terminal running while testing.

3. Discover the tools

After connecting:

1. Open the Tools tab.

2. Click List Tools.

3. Verify that the following tools are available:

   • search_papers

   • get_paper

Testing the Tools

Example 1: Search for papers

Select search_papers and enter:

{
  "query": "large language models",
  "max_results": 3
}

Click Run Tool.

Expected response: A list of up to three matching research papers, including their titles, authors, abstracts, publication dates and URLs.

Example 2: Retrieve a specific paper

Select get_paper and enter:

{
  "arxiv_id": "1706.03762"
}

Click Run Tool.

This retrieves metadata for Attention Is All You Need, the Transformer architecture paper.

How MCP Works

The Model Context Protocol provides a standardized interface through which AI applications can discover and invoke external tools.

In this project:

1. The MCP server registers Python functions as tools using the MCP SDK.

2. An MCP client connects to the server using the stdio transport.

3. The client discovers available tools and their input schemas.

4. The client invokes a tool with structured arguments.

5. The server queries arXiv and returns structured results.

The MCP Inspector acts as a development and testing client. An MCP-compatible AI assistant can use the same tools for natural-language research queries.

Technology Stack

• Python — Server implementation

• MCP Python SDK — Tool registration and protocol communication

• arxiv — Python client for the arXiv API

• uv — Dependency management

• MCP Inspector — Interactive testing and debugging

Limitations

• Searches rely on the arXiv API and require an internet connection.

• Paper retrieval returns metadata and abstracts, not full-text analysis.

• Search results are subject to arXiv API availability and rate limits.

• This repository provides the MCP server, not a standalone AI chatbot.

Future Improvements

• Automated tool testing with pytest

• Paper comparison and citation extraction

• Full-text PDF retrieval

• Integration with an MCP-compatible AI assistant

References

• Model Context Protocol

• Official Python MCP SDK

• arXiv API