Which integrations are available for this server?

Provides tools for searching and retrieving research papers from arXiv, with capabilities to query papers by topic, extract detailed paper information, and automatically save paper metadata to local storage.

How do I use MCP Research Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Research Server search for recent papers about quantum computing" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP Research Assistant 🧠

A comprehensive Model Context Protocol (MCP) setup that provides powerful tools for research, file management, and web content fetching. This project integrates multiple MCP servers to enhance your AI assistant capabilities.

✨ Features

📚 Research Tool: Search and manage academic papers from arXiv
📁 Filesystem Tool: Browse, read, and manage project files
🌐 Fetch Tool: Retrieve content from websites and APIs
🤖 Multi-LLM Support: Works with Claude, Gemini, and other AI models
💾 Local Storage: Automatically saves research data organized by topics

Related MCP server: arXiv MCP Server

🛠️ Prerequisites

Python 3.13 or higher
uv package manager (recommended) or pip
API keys for your chosen LLM providers
Claude Desktop (for MCP integration)

💻 Quick Start

1. Clone and Setup

git clone <your-repo-url> cd mcp_project

2. Install Dependencies

# Install uv if you haven't already curl -LsSf https://astral.sh/uv/install.sh | sh # Create virtual environment and install dependencies uv sync

3. Configure Environment Variables

Create a .env file in your project root:

ANTHROPIC_API_KEY=your_anthropic_api_key_here

4. Configure Claude Desktop

Create or update your Claude Desktop configuration file:

Location: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)

{ "mcpServers": { "filesystem": { "command": "npx", "args": [ "-y", "@modelcontextprotocol/server-filesystem", "." ], "cwd": "/path/to/your/mcp_project" }, "research": { "command": "/path/to/your/mcp_project/.venv/bin/python", "args": [ "/path/to/your/mcp_project/research_server.py" ], "cwd": "/path/to/your/mcp_project" }, "fetch": { "command": "/path/to/your/.local/bin/uvx", "args": ["mcp-server-fetch"], "cwd": "/path/to/your/mcp_project" } } }

Important: Replace /path/to/your/mcp_project with your actual project path.

5. Restart Claude Desktop

Restart Claude Desktop completely to load the new configuration.

🎯 How to Use

Research Tool 🔬

Search for Papers:

Search for 5 papers about machine learning

Get Paper Details:

Show me information about paper ID 1234.5678

Browse Saved Papers:

What papers do I have saved on physics?

Filesystem Tool 📁

Browse Files:

List all files in my project directory

Read Files:

Show me the contents of research_server.py

Create Files:

Create a new Python script for data analysis

Fetch Tool 🌐

Get Web Content:

Fetch the latest Python documentation

API Calls:

Get current weather data from an API

📋 Available Tools

Research Server Tools

Tool	Description	Parameters
`search_papers`	Search arXiv for papers	`topic`, `max_results`
`extract_info`	Get paper details	`paper_id`
`get_available_folders`	List saved topics	None

Filesystem Server Tools

Tool	Description
`read_file`	Read file contents
`write_file`	Write to files
`list_dir`	List directory contents
`delete_file`	Delete files

Fetch Server Tools

Tool	Description
`fetch`	Fetch content from URLs

📁 Project Structure

mcp_project/ ├── research_server.py # Main research MCP server ├── mcp_chatbot_L7.py # Chatbot with LLM integration ├── pyproject.toml # Project configuration ├── requirements.txt # Python dependencies ├── uv.lock # Dependency lock file ├── papers/ # Research data storage │ └── [topic_name]/ # Organized by topic │ └── papers_info.json # Paper metadata ├── .env # Environment variables └── README.md # This file

🔧 Configuration Details

Research Server Configuration

The research server automatically:

Creates topic-based directories in papers/
Saves paper metadata as JSON files
Provides search and retrieval functions
Integrates with arXiv API

Filesystem Server Configuration

The filesystem server:

Operates within your project directory
Provides full file management capabilities
Uses relative paths for portability

Fetch Server Configuration

The fetch server:

Handles web requests and API calls
Supports custom user agents
Can ignore robots.txt restrictions

MCP Research Assistant Working

Screenshot showing the MCP Research Assistant successfully running with all tools working

📝 Development

Adding New Tools

Edit research_server.py to add new functions
Use the @mcp.tool() decorator
Test with MCP Inspector
Update documentation

Customizing LLM Behavior

Edit mcp_chatbot_L7.py
Modify tool descriptions and parameters
Add custom prompts and resources

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MCP Research Server